人工智能学术速递[10.18]

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cs.AI人工智能，共计54篇

【1】 Textual Backdoor Attacks Can Be More Harmful via Two Simple Tricks 标题：通过两个简单的技巧，文本后门攻击的危害可能更大 链接：https://arxiv.org/abs/2110.08247

作者：Yangyi Chen,Fanchao Qi,Zhiyuan Liu,Maosong Sun 机构：Department of Computer Science and Technology, Tsinghua University, Beijing, China, Beijing National Research Center for Information Science and Technology, Institute for Artificial Intelligence, Tsinghua University, Beijing, China 备注：Work in progress 摘要：后门攻击是深度学习中的一种紧急安全威胁。当一个深度神经模型被注入后门时，它将在标准输入上正常运行，但一旦输入包含特定的后门触发器，它将给出对手指定的预测。当前的文本后门攻击在某些困难情况下具有较差的攻击性能。在本文中，我们发现两个简单的技巧可以使现有的文本后门攻击更加有害。第一个技巧是在受害者模型的训练过程中增加一个额外的训练任务来区分中毒数据和干净数据，第二个技巧是使用所有干净的训练数据，而不是删除中毒数据对应的原始干净数据。这两个技巧普遍适用于不同的攻击模型。我们在三种困难的情况下进行实验，包括干净的数据微调、低中毒率和标签一致性攻击。实验结果表明，这两种技巧可以显著提高攻击性能。本文展示了后门攻击的巨大潜在危害。所有代码和数据将公开，以便于进一步研究。 摘要：Backdoor attacks are a kind of emergent security threat in deep learning. When a deep neural model is injected with a backdoor, it will behave normally on standard inputs but give adversary-specified predictions once the input contains specific backdoor triggers. Current textual backdoor attacks have poor attack performance in some tough situations. In this paper, we find two simple tricks that can make existing textual backdoor attacks much more harmful. The first trick is to add an extra training task to distinguish poisoned and clean data during the training of the victim model, and the second one is to use all the clean training data rather than remove the original clean data corresponding to the poisoned data. These two tricks are universally applicable to different attack models. We conduct experiments in three tough situations including clean data fine-tuning, low poisoning rate, and label-consistent attacks. Experimental results show that the two tricks can significantly improve attack performance. This paper exhibits the great potential harmfulness of backdoor attacks. All the code and data will be made public to facilitate further research.

【2】 LPRules: Rule Induction in Knowledge Graphs Using Linear Programming 标题：LPRules：基于线性规划的知识图规则归纳 链接：https://arxiv.org/abs/2110.08245

作者：Sanjeeb Dash,Joao Goncalves 摘要：知识图（KG）完备性是人工智能中一个被广泛研究的问题。基于规则的方法和基于嵌入的方法形成了两种解决方案技术。基于规则的方法学习捕获输入图中现有事实的一阶逻辑规则，然后使用这些规则对缺失的事实进行推理。这种方法的一个主要缺点是缺乏对大型数据集的可伸缩性。在本文中，我们提出了一个简单的线性规划（LP）模型，从候选规则列表中选择规则并为其分配权重。对于较小的KG，我们使用简单的启发式方法创建候选列表。对于较大的KG，我们从一个小的初始候选列表开始，然后使用标准列生成思想添加更多规则，以改进LP模型的目标值。为了提高可解释性和通用性，我们通过显式约束限制所选规则集的复杂性，并调整单个数据集的复杂性超参数。我们表明，我们的方法可以为四分之三的广泛使用的KG数据集获得最先进的结果，同时比其他流行的规则学习者（包括一些基于神经符号的方法）花费更少的计算时间。我们的方法改进的可伸缩性使我们能够处理大型数据集，如YAGO3-10。 摘要：Knowledge graph (KG) completion is a well-studied problem in AI. Rule-based methods and embedding-based methods form two of the solution techniques. Rule-based methods learn first-order logic rules that capture existing facts in an input graph and then use these rules for reasoning about missing facts. A major drawback of such methods is the lack of scalability to large datasets. In this paper, we present a simple linear programming (LP) model to choose rules from a list of candidate rules and assign weights to them. For smaller KGs, we use simple heuristics to create the candidate list. For larger KGs, we start with a small initial candidate list, and then use standard column generation ideas to add more rules in order to improve the LP model objective value. To foster interpretability and generalizability, we limit the complexity of the set of chosen rules via explicit constraints, and tune the complexity hyperparameter for individual datasets. We show that our method can obtain state-of-the-art results for three out of four widely used KG datasets, while taking significantly less computing time than other popular rule learners including some based on neuro-symbolic methods. The improved scalability of our method allows us to tackle large datasets such as YAGO3-10.

【3】 Influencing Towards Stable Multi-Agent Interactions 标题：对稳定的多智能体交互的影响 链接：https://arxiv.org/abs/2110.08229

作者：Woodrow Z. Wang,Andy Shih,Annie Xie,Dorsa Sadigh 备注：15 pages, 5 figures, Published as an Oral at Conference on Robot Learning (CoRL) 2021 摘要：在多智能体环境中学习是困难的，因为对手或伙伴的变化行为会引入非平稳性。我们提出了一种算法来主动影响另一个代理的策略以使其稳定下来，而不是被动地适应另一个代理（对手或伙伴）的行为，这可以抑制由另一个代理引起的非平稳性。我们学习了另一个智能体策略的低维潜在表示，以及潜在策略相对于我们机器人行为的演化动力学。有了这个学习过的动力学模型，我们可以定义一个无监督的稳定性奖励，来训练我们的机器人故意影响另一个代理朝着单一策略稳定下来。我们证明了在各种模拟环境中，包括自动驾驶、紧急通信和机器人操作，稳定在提高任务报酬最大化效率方面的有效性。我们在网站上展示定性结果：https://sites.google.com/view/stable-marl/. 摘要：Learning in multi-agent environments is difficult due to the non-stationarity introduced by an opponent's or partner's changing behaviors. Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to proactively influence the other agent's strategy to stabilize -- which can restrain the non-stationarity caused by the other agent. We learn a low-dimensional latent representation of the other agent's strategy and the dynamics of how the latent strategy evolves with respect to our robot's behavior. With this learned dynamics model, we can define an unsupervised stability reward to train our robot to deliberately influence the other agent to stabilize towards a single strategy. We demonstrate the effectiveness of stabilizing in improving efficiency of maximizing the task reward in a variety of simulated environments, including autonomous driving, emergent communication, and robotic manipulation. We show qualitative results on our website: https://sites.google.com/view/stable-marl/.

【4】 Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text 标题：跨域数据集成在生物医学文本命名实体消歧中的应用 链接：https://arxiv.org/abs/2110.08228

作者：Maya Varma,Laurel Orr,Sen Wu,Megan Leszczynski,Xiao Ling,Christopher Ré 机构：Stanford University, Apple 备注：Accepted to Findings of EMNLP 2021 摘要：命名实体消歧（NED）涉及将文本提及映射到结构化实体，由于罕见实体的存在，在医学领域尤其具有挑战性。现有方法受到生物医学知识库中粗粒度结构资源的限制，以及对不常见资源覆盖率较低的训练数据集的使用。在这项工作中，我们通过提出一种跨领域数据集成方法来解决这些问题，该方法将结构知识从通用文本知识库转移到医学领域。我们利用我们的集成方案来增加结构资源，并生成一个用于预训练的大型生物医学NED数据集。我们的预训练模型结合注入的结构知识，在两个基准医学NED数据集（MedNeds和BC5CDR）上实现了最先进的性能。此外，我们将罕见实体的消歧提高了57个精度点。 摘要：Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points.

【5】 Shared Visual Representations of Drawing for Communication: How do different biases affect human interpretability and intent? 标题：交流绘画的共享视觉表征：不同的偏见如何影响人类的解释力和意图？ 链接：https://arxiv.org/abs/2110.08203

作者：Daniela Mihai,Jonathon Hare 机构：Electronics and Computer Science, The University of Southampton, Southampton, UK 摘要：我们提出了一项调查，调查如何代表性损失可以影响绘画制作的人工代理玩通信游戏。基于最近的进展，我们表明，强大的预训练编码器网络与适当的感应偏差相结合，可以使代理绘制可识别的草图，同时仍能进行良好的通信。此外，我们开始开发一种方法来帮助自动分析草图所传达的语义内容，并证明，尽管agent训练是自我监督的，但当前诱导感知偏差的方法导致了对象性是一个关键特征的概念。 摘要：We present an investigation into how representational losses can affect the drawings produced by artificial agents playing a communication game. Building upon recent advances, we show that a combination of powerful pretrained encoder networks, with appropriate inductive biases, can lead to agents that draw recognisable sketches, whilst still communicating well. Further, we start to develop an approach to help automatically analyse the semantic content being conveyed by a sketch and demonstrate that current approaches to inducing perceptual biases lead to a notion of objectness being a key feature despite the agent training being self-supervised.

【6】 Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System 标题：工业联合学习系统中超参数优化方法的评价 链接：https://arxiv.org/abs/2110.08202

作者：Stephanie Holly,Thomas Hiessl,Safoura Rezapour Lakani,Daniel Schall,Clemens Heitzinger,Jana Kemnitz 机构：Siemens Technology and, TU Wien 备注：This paper is accepted at the IDSC this https URL and will be published by Springer. The Version uploaded is before the peer review process. The link to the final version will be updated as soon as the paper is published 摘要：联合学习（FL）将模型训练与直接访问数据的需求分离，并允许组织与行业合作伙伴协作，以达到令人满意的性能水平，而无需共享易受攻击的业务信息。机器学习算法的性能对其超参数的选择非常敏感。在FL环境中，超参数优化带来了新的挑战。在这项工作中，我们研究了FL系统中不同超参数优化方法的影响。为了降低通信成本，这是FL中的一个关键瓶颈，我们研究了一种局部超参数优化方法，与全局超参数优化方法相比，它允许每个客户机都有自己的超参数配置。我们基于网格搜索和贝叶斯优化实现了这些方法，并在使用i.i.d.分区的MNIST数据集和使用非i.i.d.分区的基于物联网（IoT）传感器的工业数据集上评估了算法。 摘要：Federated Learning (FL) decouples model training from the need for direct access to the data and allows organizations to collaborate with industry partners to reach a satisfying level of performance without sharing vulnerable business information. The performance of a machine learning algorithm is highly sensitive to the choice of its hyperparameters. In an FL setting, hyperparameter optimization poses new challenges. In this work, we investigated the impact of different hyperparameter optimization approaches in an FL system. In an effort to reduce communication costs, a critical bottleneck in FL, we investigated a local hyperparameter optimization approach that -- in contrast to a global hyperparameter optimization approach -- allows every client to have its own hyperparameter configuration. We implemented these approaches based on grid search and Bayesian optimization and evaluated the algorithms on the MNIST data set using an i.i.d. partition and on an Internet of Things (IoT) sensor based industrial data set using a non-i.i.d. partition.

【7】 Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series 标题：基于深度学习的卫星时间序列地块分类轮换模型 链接：https://arxiv.org/abs/2110.08187

作者：Félix Quinton,Loic Landrieu 机构：LASTIG, Univ. Gustave Eiffel, ENSG, IGN, F-, Saint-Mand´e, France 备注：Under review 摘要：虽然年度作物轮作对农业优化起着至关重要的作用，但在作物类型自动制图中，它们在很大程度上被忽略了。在本文中，我们利用注释卫星数据数量的增加，提出了第一种深度学习方法，同时对地块分类的年际和年际农业动态进行建模。除了简单的训练调整外，我们的模型比当前最先进的作物分类技术提高了660多万个点。此外，我们发布了第一个大型多年农业数据集，其中包含超过300000个带注释的地块。 摘要：While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose the first deep learning approach modeling simultaneously the inter- and intra-annual agricultural dynamics of parcel classification. Along with simple training adjustments, our model provides an improvement of over 6.6 mIoU points over the current state-of-the-art of crop classification. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.

【8】 Simulation of emergence in artificial societies: a practical model-based approach with the EB-DEVS formalism 标题：人工社会中涌现的模拟：采用EB-DEVS形式主义的一种实用的基于模型的方法 链接：https://arxiv.org/abs/2110.08170

作者：Daniel Foguelman,Esteban Lanzarotti,Emanuel Ferreyra,Rodrigo Castro 机构：CONICET-Universidad de Buenos Aires, Instituto de Investigación en, Ciencias de la Computación (ICC), Facultad de Ciencias Exactas y Naturales, Departamento de Computación., Buenos Aires, Argentina., Instituto de Cálculo (IC). 摘要：复杂系统的建模和仿真是探索和理解社会过程的关键，得益于形式化机制，从局部层面的互动中获得全局层面的属性。在本文中，我们通过应用EB-DEVS扩展了复杂系统中形式化方法的知识体系，EB-DEVS是一种新的形式主义，专门用于紧急属性的建模、仿真和实时识别。我们引导读者通过对不同社会系统实施不同的经典模型来介绍良好的建模实践，并展示使用EB-DEV建模涌现的优势和局限性，特别是通过其实时涌现检测能力。这项工作提供了案例研究驱动的证据，证明了建模沟通结构的方法的整洁性和紧凑性，这些沟通结构可以是显性的或隐性的，静态的或动态的，有无多级互动，以及弱或强的紧急行为。我们通过实例表明，EB-DEV允许在需要时通过纳入紧急行为来对所分析的社会进行概念化，即将Sugarscape模型中的基尼指数、文化传播模型中的时尚和时尚整合为宏观集合，偏好依恋模型中的尺寸偏差度分布、分离模型中的幸福指数和SIR流行病模型中的隔离。在每个示例中，我们讨论了通信结构在多级仿真模型开发中的作用，并说明了微观-宏观反馈回路如何实现宏观级属性的建模。我们的结果强调了多级特征的相关性，以支持复杂系统建模和仿真中的稳健方法。 摘要：Modelling and simulation of complex systems is key to exploring and understanding social processes, benefiting from formal mechanisms to derive global-level properties from local-level interactions. In this paper we extend the body of knowledge on formal methods in complex systems by applying EB-DEVS, a novel formalism tailored for the modelling, simulation and live identification of emergent properties. We guide the reader through the implementation of different classical models for varied social systems to introduce good modelling practices and showcase the advantages and limitations of modelling emergence with EB-DEVS, in particular through its live emergence detection capability. This work provides case study-driven evidence for the neatness and compactness of the approach to modelling communication structures that can be explicit or implicit, static or dynamic, with or without multilevel interactions, and with weak or strong emergent behaviour. Throughout examples we show that EB-DEVS permits conceptualising the analysed societies by incorporating emergent behaviour when required, namely by integrating as a macro-level aggregate the Gini index in the Sugarscape model, Fads and Fashion in the Dissemination of Culture model, size-biased degree distribution in a Preferential Attachment model, happiness index in the Segregation model and quarantines in the SIR epidemic model. In each example we discuss the role of communication structures in the development of multilevel simulation models, and illustrate how micro-macro feedback loops enable the modelling of macro-level properties. Our results stress the relevance of multilevel features to support a robust approach in the modelling and simulation of complex systems.

【9】 Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction 标题：集成多种提取路径的迭代预测多语言开放信息抽取 链接：https://arxiv.org/abs/2110.08144

作者：Bhushan Kotnis,Kiril Gashteovski,Carolin Lawrence,Daniel Oñoro Rubio,Vanesa Rodriguez-Tembras,Makoto Takamoto,Mathias Niepert 机构：NEC Laboratories Europe, Heidelberg, Germany., Heidelberg University, Center for Iberoamerican Studies, Germany. 摘要：在本文中，我们研究了开放信息提取（OpenIE）任务的一个简单假设，即如果提取是以更容易提取的先前提取为条件，则提取三元组的某些元素可能更容易。我们成功地利用了这一点，并提出了一个神经多语言OpenIE系统，该系统通过在三元组的不同元素上调节提取来迭代提取三元组，从而产生丰富的提取集。MiLIE的迭代特性还允许将基于规则的提取系统与神经端到端系统无缝集成，从而提高性能。MiLIE在从汉语到加利西亚语的多种语言上都优于SOTA系统，这得益于它能够结合多种提取途径。我们的分析证实，提取的某些元素确实比其他元素更容易提取。最后，我们介绍了两种低资源语言，即日语和加利西亚语的OpenIE评估数据集。 摘要：In this paper we investigate a simple hypothesis for the Open Information Extraction (OpenIE) task, that it may be easier to extract some elements of an triple if the extraction is conditioned on prior extractions which may be easier to extract. We successfully exploit this and propose a neural multilingual OpenIE system that iteratively extracts triples by conditioning extractions on different elements of the triple leading to a rich set of extractions. The iterative nature of MiLIE also allows for seamlessly integrating rule based extraction systems with a neural end-to-end system leading to improved performance. MiLIE outperforms SOTA systems on multiple languages ranging from Chinese to Galician thanks to it's ability of combining multiple extraction pathways. Our analysis confirms that it is indeed true that certain elements of an extraction are easier to extract than others. Finally, we introduce OpenIE evaluation datasets for two low resource languages namely Japanese and Galician.

【10】 Towards a Multi-Agent System Architecture for Supply Chain Management 标题：面向供应链管理的多Agent系统体系结构研究 链接：https://arxiv.org/abs/2110.08125

作者：Carlos R. Jaimez-González,Wulfrano A. Luna-Ramírez 机构：Departamento de Tecnologías de la Información, Universidad Autónoma Metropolitana -, Cuajimalpa, Av. Constituyentes No. , Col. Lomas Altas, C.P. , México D.F. 备注：None 摘要：自互联网诞生以来，各个业务流程一直在发生变化，在电子商务环境中，这些业务流程正在适应竞争和不断变化的市场条件，并朝着更加分散和协作的业务模式发展。本文提出了一种用于供应链管理的多代理系统架构，该架构探索了分布式电子商务环境中的不同策略并提供了解决方案。该系统旨在支持不同类型的接口，允许与其他已开发的业务模型进行互操作。为了展示整个多agent系统是如何开发的，本文介绍并解释了协作agent的实现。 摘要：Individual business processes have been changing since the Internet was created, and they are now oriented towards a more distributed and collaborative business model, in an e-commerce environment that adapts itself to the competitive and changing market conditions. This paper presents a multi-agent system architecture for supply chain management, which explores different strategies and offers solutions in a distributed e-commerce environment. The system is designed to support different types of interfaces, which allow interoperating with other business models already developed. In order to show how the entire multi-agent system is being developed, the implementation of a collaborative agent is presented and explained.

【11】 Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learning 标题：基于多智能体深度强化学习的高速公路交织区分散协同换道 链接：https://arxiv.org/abs/2110.08124

作者：Yi Hou,Peter Graf 机构：L 摘要：高速公路瓶颈（如合流区和交织区）拥堵期间频繁的车道变换进一步降低了道路通行能力。深度强化学习（RL）和互联自动车辆技术的出现为通过合作换道提高高速公路瓶颈处的机动性和能源效率提供了可能的解决方案。Deep RL是一组机器学习方法，使代理能够通过从环境中学习来提高其性能。在本研究中，采用多智能体深度RL范式，利用近端策略优化设计了一种分散式协作式车道变换控制器。在分散控制策略中，策略学习和行动奖励是局部评估的，每个代理（车辆）都可以访问全局状态信息。多agent deep-RL比单agent deep-RL需要更少的计算资源，并且比单agent deep-RL更具可扩展性，使其成为时间敏感应用（如协作车道变换）的强大工具。这项研究的结果表明，在交通流量、车速、每辆车的停车次数、车辆燃油效率和排放方面，由多智能体深度RL实现的合作换道比人类驾驶员具有更高的性能。经过训练的RL策略是可转移的，可以推广到未拥挤、中度拥挤和极度拥挤的交通条件。 摘要：Frequent lane changes during congestion at freeway bottlenecks such as merge and weaving areas further reduce roadway capacity. The emergence of deep reinforcement learning (RL) and connected and automated vehicle technology provides a possible solution to improve mobility and energy efficiency at freeway bottlenecks through cooperative lane changing. Deep RL is a collection of machine-learning methods that enables an agent to improve its performance by learning from the environment. In this study, a decentralized cooperative lane-changing controller was developed using proximal policy optimization by adopting a multi-agent deep RL paradigm. In the decentralized control strategy, policy learning and action reward are evaluated locally, with each agent (vehicle) getting access to global state information. Multi-agent deep RL requires lower computational resources and is more scalable than single-agent deep RL, making it a powerful tool for time-sensitive applications such as cooperative lane changing. The results of this study show that cooperative lane changing enabled by multi-agent deep RL yields superior performance to human drivers in term of traffic throughput, vehicle speed, number of stops per vehicle, vehicle fuel efficiency, and emissions. The trained RL policy is transferable and can be generalized to uncongested, moderately congested, and extremely congested traffic conditions.

【12】 Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation 标题：进化强化学习中不同优化方案对不同行为生成的影响 链接：https://arxiv.org/abs/2110.08122

作者：Victor Villin,Naoki Masuyama,Yusuke Nojima 机构：Dept. of Computer Science and, Intelligent Systems, Osaka Prefecture University, Sakai, Osaka, Japan 摘要：为给定任务生成各种策略是一项挑战。然而，它已经被证明为主要的学习过程带来了很多好处，比如改进的行为探索。随着对进化计算和强化学习中解的异质性的兴趣的增长，出现了许多有前途的方法。为了更好地理解一个人如何引导多个策略走向不同的策略并从多样性中获益，我们需要进一步分析奖励信号调制和其他进化机制对获得的行为的影响。为此，本文考虑了一个现有的进化强化学习框架，该框架利用多目标优化作为一种获得策略的方法，能够成功地完成与行为相关的任务并完成主要目标。雅达利奥运会上的实验强调，不考虑目标的优化配方同样会产生多样性，甚至是在解决手头问题时更糟糕的输出代理，而不管所获得的行为如何。 摘要：Generating various strategies for a given task is challenging. However, it has already proven to bring many assets to the main learning process, such as improved behavior exploration. With the growth in the interest of heterogeneity in solution in evolutionary computation and reinforcement learning, many promising approaches have emerged. To better understand how one guides multiple policies toward distinct strategies and benefit from diversity, we need to analyze further the influence of the reward signal modulation and other evolutionary mechanisms on the obtained behaviors. To that effect, this paper considers an existing evolutionary reinforcement learning framework which exploits multi-objective optimization as a way to obtain policies that succeed at behavior-related tasks as well as completing the main goal. Experiments on the Atari games stress that optimization formulations which do not consider objectives equally fail at generating diversity and even output agents that are worse at solving the problem at hand, regardless of the obtained behaviors.

【13】 Few-Shot Bot: Prompt-Based Learning for Dialogue Systems 标题：Few-Shot机器人：对话系统中基于提示的学习 链接：https://arxiv.org/abs/2110.08118

作者：Andrea Madotto,Zhaojiang Lin,Genta Indra Winata,Pascale Fung 机构：Department of Electronics and Computer Engineering, The Hong Kong University of Science and Technology 摘要：在会话人工智能中，学习仅使用几个例子进行会话是一个巨大的挑战。当前最好的会话模型是在大型会话数据集上进行微调的语言模型（LMs），它们要么是良好的聊天工具（如BlenderBot），要么是面向目标的系统（如MinTL）。训练这些模型在计算资源和时间上都是昂贵的，而且很难用新的会话技能使它们跟上时代。一个简单但尚未探索的解决方案是基于即时的少量快照学习（Brown et al.2020），它不需要基于梯度的微调，而是使用LM上下文中的一些示例作为唯一的学习来源。在这篇论文中，我们探讨了在对话任务中基于提示的Few-Shot学习。我们在九个响应生成任务中测试了不同大小的LMs，其中包括四个基于知识的任务、一个面向任务的生成任务、三个开放式聊天任务和受控风格生成任务，以及五个会话解析任务，其中包括对话状态跟踪、图形路径生成、人物角色信息提取、，文档检索和internet查询生成。目前发布的最大的LM（GPT-J-6B）采用基于提示的少量射击学习，因此无需训练，与经过充分训练的最先进模型相比，其性能具有竞争力。此外，我们提出了一种新的基于提示的Few-Shot分类器，该分类器也不需要任何微调，可以根据对话历史选择最合适的提示。最后，通过结合基于提示的少数镜头学习和技能选择器的功能，我们创建了一个名为少数镜头机器人（FSB）的端到端聊天机器人，它自动选择最合适的会话技能，查询不同的知识库或互联网，并使用检索到的知识生成类似人类的响应，每个技能都只使用很少的对话示例。 摘要：Learning to converse using only a few examples is a great challenge in conversational AI. The current best conversational models, which are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL), are language models (LMs) fine-tuned on large conversational datasets. Training these models is expensive, both in terms of computational resources and time, and it is hard to keep them up to date with new conversational skills. A simple yet unexplored solution is prompt-based few-shot learning (Brown et al. 2020) which does not require gradient-based fine-tuning but instead uses a few examples in the LM context as the only source of learning. In this paper, we explore prompt-based few-shot learning in dialogue tasks. We benchmark LMs of different sizes in nine response generation tasks, which include four knowledge-grounded tasks, a task-oriented generations task, three open-chat tasks, and controlled stylistic generation, and five conversational parsing tasks, which include dialogue state tracking, graph path generation, persona information extraction, document retrieval, and internet query generation. The current largest released LM (GPT-J-6B) using prompt-based few-shot learning, and thus requiring no training, achieves competitive performance to fully trained state-of-the-art models. Moreover, we propose a novel prompt-based few-shot classifier, that also does not require any fine-tuning, to select the most appropriate prompt given a dialogue history. Finally, by combining the power of prompt-based few-shot learning and a Skill Selector, we create an end-to-end chatbot named the Few-Shot Bot (FSB), which automatically selects the most appropriate conversational skill, queries different knowledge bases or the internet, and uses the retrieved knowledge to generate a human-like response, all using only few dialogue examples per skill.

【14】 Using DeepProbLog to perform Complex Event Processing on an Audio Stream 标题：使用DeepProbLog对音频流进行复杂事件处理 链接：https://arxiv.org/abs/2110.08090

作者：Marc Roig Vilamala,Tianwei Xing,Harrison Taylor,Luis Garcia,Mani Srivastava,Lance Kaplan,Alun Preece,Angelika Kimmig,Federico Cerutti 机构：Cardiff University, University of California, Los Angeles, Army Research Laboratory, KU Leuven, Department of Computer Science; Leuven.AI, University of Brescia 备注：8 pages, 3 figures 摘要：本文提出了一种基于DeepProbLog的复杂事件处理（CEP）方法。该方法具有以下目标：（i）允许使用子符号数据作为输入，（ii）保留复杂事件规则定义的灵活性和模块性，（iii）允许以端到端的方式对系统进行训练，以及（iv）对噪声标记数据具有鲁棒性。我们的方法利用DeepProbLog创建一个神经符号架构，该架构将神经网络与概率逻辑层结合起来处理子符号数据，以允许用户定义复杂事件的规则。我们证明了我们的方法能够从音频流中检测复杂事件。我们还证明了我们的方法即使在具有中等比例噪声数据的数据集上也能够进行训练。 摘要：In this paper, we present an approach to Complex Event Processing (CEP) that is based on DeepProbLog. This approach has the following objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining the flexibility and modularity on the definitions of complex event rules, (iii) allowing the system to be trained in an end-to-end manner and (iv) being robust against noisily labelled data. Our approach makes use of DeepProbLog to create a neuro-symbolic architecture that combines a neural network to process the subsymbolic data with a probabilistic logic layer to allow the user to define the rules for the complex events. We demonstrate that our approach is capable of detecting complex events from an audio stream. We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.

【15】 Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification 标题：基于卷积神经网络图像分类的真空隔热玻璃质量自动控制 链接：https://arxiv.org/abs/2110.08079

作者：Henrik Riedel,Sleheddine Mokdad,Isabell Schulz,Cenk Kocer,Philipp Rosendahl,Jens Schneider,Michael A. Kraus,Michael Drass 机构：the date of receipt and acceptance should be inserted later 备注：10 pages, 11 figures, 1 table 摘要：真空隔热玻璃（VIG）是一种高度隔热的窗户技术，与同等性能的充气隔热玻璃相比，它具有极薄的外形和较轻的重量。VIG是双窗格玻璃配置，窗格玻璃之间有亚毫米真空间隙，因此在其使用寿命期间处于恒定大气压力下。小柱子位于窗格玻璃之间以保持间隙，这会损坏玻璃，从而缩短VIG装置的使用寿命。为了有效地评估玻璃上的任何表面损伤，非常需要一个自动损伤检测系统。为了对损伤进行分类，我们使用卷积神经网络开发、训练和测试了一个深度学习计算机视觉系统。分类模型完美地对测试数据集进行了分类，接收器工作特性（ROC）的曲线下面积（AUC）为100%。通过使用更快的RCNN定位支柱的位置，我们自动将图像裁剪为相关信息。我们采用可解释人工智能（XAI）的Grad-CAM和Score-CAM这两种最先进的方法来理解内部机制，并能够证明我们的分类器在识别裂纹位置和几何形状方面优于ResNet50V2。因此，即使没有大量的训练数据，所提出的方法也可以用于检测系统缺陷。对我们模型预测能力的进一步分析表明，在收敛速度、准确性、100%召回率下的精确度以及ROC的AUC方面，我们的模型优于最先进的模型（ResNet50V2、ResNet101V2和ResNet152V2）。 摘要：Vacuum Insulated Glazing (VIG) is a highly thermally insulating window technology, which boasts an extremely thin profile and lower weight as compared to gas-filled insulated glazing units of equivalent performance. The VIG is a double-pane configuration with a submillimeter vacuum gap between the panes and therefore under constant atmospheric pressure over their service life. Small pillars are positioned between the panes to maintain the gap, which can damage the glass reducing the lifetime of the VIG unit. To efficiently assess any surface damage on the glass, an automated damage detection system is highly desirable. For the purpose of classifying the damage, we have developed, trained, and tested a deep learning computer vision system using convolutional neural networks. The classification model flawlessly classified the test dataset with an area under the curve (AUC) for the receiver operating characteristic (ROC) of 100%. We have automatically cropped the images down to their relevant information by using Faster-RCNN to locate the position of the pillars. We employ the state-of-the-art methods Grad-CAM and Score-CAM of explainable Artificial Intelligence (XAI) to provide an understanding of the internal mechanisms and were able to show that our classifier outperforms ResNet50V2 for identification of crack locations and geometry. The proposed methods can therefore be used to detect systematic defects even without large amounts of training data. Further analyses of our model's predictive capabilities demonstrates its superiority over state-of-the-art models (ResNet50V2, ResNet101V2 and ResNet152V2) in terms of convergence speed, accuracy, precision at 100% recall and AUC for ROC.

【16】 SAT Encodings for Pseudo-Boolean Constraints Together With At-Most-One Constraints 标题：伪布尔约束的SAT编码以及至多一个约束 链接：https://arxiv.org/abs/2110.08068

作者：Miquel Bofill,Jordi Coll,Peter Nightingale,Josep Suy,Felix Ulrich-Oltean,Mateu Villaret 机构：Universitat de Girona, Girona, Spain, Aix Marseille Univ, Universit´e de Toulon, CNRS, LIS, Marseille, France, University of York, York, United Kingdom 摘要：当使用命题可满足性（SAT）解决组合问题时，问题的编码至关重要。我们研究伪布尔（PB）约束的编码，这是一种常见的算术约束，出现在各种组合问题中，如时间表、调度和资源分配。在某些情况下，PB约束与其变量子集上最多一个（AMO）约束一起出现（形成PB（AMO）约束）。最近的工作表明，在使用决策图编码PB约束时考虑AMOs可以显著提高求解效率。在本文中，我们将该方法扩展到PB约束的其他最新编码，开发了几种新的PB（AMO）约束编码。此外，我们还提供了一种更紧凑、更高效的通用累加器编码版本，名为简化的通用累加器。这种新的编码也适用于PB（AMO）约束，以获得进一步的增益。我们的实验表明，PB（AMO）约束的编码可以大大小于PB约束的编码。PB（AMO）编码允许在一个时间限制内解决更多的实例，在某些情况下，解决时间可以提高一个数量级以上。我们还观察到，在所考虑的编码中没有单一的总赢家，但每个编码的效率可能取决于PB（AMO）特性，例如系数值的大小。 摘要：When solving a combinatorial problem using propositional satisfiability (SAT), the encoding of the problem is of vital importance. We study encodings of Pseudo-Boolean (PB) constraints, a common type of arithmetic constraint that appears in a wide variety of combinatorial problems such as timetabling, scheduling, and resource allocation. In some cases PB constraints occur together with at-most-one (AMO) constraints over subsets of their variables (forming PB(AMO) constraints). Recent work has shown that taking account of AMOs when encoding PB constraints using decision diagrams can produce a dramatic improvement in solver efficiency. In this paper we extend the approach to other state-of-the-art encodings of PB constraints, developing several new encodings for PB(AMO) constraints. Also, we present a more compact and efficient version of the popular Generalized Totalizer encoding, named Reduced Generalized Totalizer. This new encoding is also adapted for PB(AMO) constraints for a further gain. Our experiments show that the encodings of PB(AMO) constraints can be substantially smaller than those of PB constraints. PB(AMO) encodings allow many more instances to be solved within a time limit, and solving time is improved by more than one order of magnitude in some cases. We also observed that there is no single overall winner among the considered encodings, but efficiency of each encoding may depend on PB(AMO) characteristics such as the magnitude of coefficient values.

【17】 Dual-Arm Adversarial Robot Learning 标题：双臂对抗性机器人学习 链接：https://arxiv.org/abs/2110.08066

作者：Elie Aljalbout 机构：Technical University of Munich 备注：Accepted at CoRL 2021, Blue Sky Track 摘要：机器人学习是未来自动化和机器智能的一个非常有前途的课题。未来的机器人应该能够自主地获得技能，学会表现自己的环境，并与之互动。虽然这些主题已经在仿真中进行了探索，但现实世界中的机器人学习研究似乎仍然有限。这是由于在现实世界中遇到的额外挑战，如噪声传感器和执行器、安全探测、非平稳动力学、自主环境重置以及长时间运行实验的成本。除非我们为这些问题开发出可扩展的解决方案，否则学习涉及手眼协调和丰富接触的复杂任务将仍然是一个未触及的愿景，只有在受控实验室环境中才可行。我们建议将双臂设置作为机器人学习的平台。这样的设置可以安全地收集数据，以获取操作技能，并以机器人监督的方式训练感知模块。它们还简化了重置环境的过程。此外，对抗式学习通过最大化基于博弈论目标的探索，同时确保基于协作任务空间的安全性，有可能提高机器人学习方法的泛化能力。在本文中，我们将讨论这种设置的潜在好处以及可以追求的挑战和研究方向。 摘要：Robot learning is a very promising topic for the future of automation and machine intelligence. Future robots should be able to autonomously acquire skills, learn to represent their environment, and interact with it. While these topics have been explored in simulation, real-world robot learning research seems to be still limited. This is due to the additional challenges encountered in the real-world, such as noisy sensors and actuators, safe exploration, non-stationary dynamics, autonomous environment resetting as well as the cost of running experiments for long periods of time. Unless we develop scalable solutions to these problems, learning complex tasks involving hand-eye coordination and rich contacts will remain an untouched vision that is only feasible in controlled lab environments. We propose dual-arm settings as platforms for robot learning. Such settings enable safe data collection for acquiring manipulation skills as well as training perception modules in a robot-supervised manner. They also ease the processes of resetting the environment. Furthermore, adversarial learning could potentially boost the generalization capability of robot learning methods by maximizing the exploration based on game-theoretic objectives while ensuring safety based on collaborative task spaces. In this paper, we will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.

【18】 Detecting Modularity in Deep Neural Networks 标题：深度神经网络中的模块性检测 链接：https://arxiv.org/abs/2110.08058

作者：Shlomi Hod,Stephen Casper,Daniel Filan,Cody Wild,Andrew Critch,Stuart Russell 机构：Center for Human-Compatible AI (CHAI), University of California Berkeley, Boston University, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), ∗ Equal contribution 备注：Code is available at this https URL 摘要：神经网络是模块化的，其计算图的部分（即结构）可以表示为执行与总体任务（即功能）相关的一些可理解的子任务。现代深层神经网络是模块化的吗？如何量化这一点？在本文中，我们考虑的问题评估模块化所表现出的网络神经元的分割。我们为此提出了两个代理：重要性，它反映了神经元组对网络性能的重要性；和连贯性，这反映了他们的神经元如何一致地与输入的特征相联系。为了测量这些代理，我们开发了一套统计方法，基于传统上用于解释单个神经元的技术。我们将代理应用于通过对网络神经元的图形表示进行频谱聚类而生成的划分，其边缘由网络权重或激活相关性确定。我们表明，这些划分，甚至是仅基于权重的划分（即严格地从非运行时分析），揭示了重要且连贯的神经元组。这些结果表明，基于图的划分可以揭示模块性，并帮助我们理解深层神经网络的功能。 摘要：A neural network is modular to the extent that parts of its computational graph (i.e. structure) can be represented as performing some comprehensible subtask relevant to the overall task (i.e. functionality). Are modern deep neural networks modular? How can this be quantified? In this paper, we consider the problem of assessing the modularity exhibited by a partitioning of a network's neurons. We propose two proxies for this: importance, which reflects how crucial sets of neurons are to network performance; and coherence, which reflects how consistently their neurons associate with features of the inputs. To measure these proxies, we develop a set of statistical methods based on techniques conventionally used to interpret individual neurons. We apply the proxies to partitionings generated by spectrally clustering a graph representation of the network's neurons with edges determined either by network weights or correlations of activations. We show that these partitionings, even ones based only on weights (i.e. strictly from non-runtime analysis), reveal groups of neurons that are important and coherent. These results suggest that graph-based partitioning can reveal modularity and help us understand how deep neural networks function.

【19】 Learning Semantics: An Opportunity for Effective 6G Communications 标题：学习语义：有效开展6G通信的机会 链接：https://arxiv.org/abs/2110.08049

作者：Mohamed Sana,Emilio Calvanese Strinati 机构：CEA-Leti, Universit´e Grenoble Alpes, F-, Grenoble, France 备注：Accepted for publication at IEEE CCNC 2021 摘要：最近，语义通信被认为是未来6G网络的关键促成因素。回到香农的信息理论，通信的目标长期以来一直是保证正确接收传输的信息，而不管其含义如何。然而，一般来说，每当通信发生以传达意义时，重要的是接收者对传输的消息的理解，而不一定是其正确的重构。因此，语义通信引入了一种新的模式：只传输足够让接收者捕捉到预期含义的相关信息可以节省大量的通信带宽。因此，这项工作探索了语义通信为5G网络以外的网络提供的机会。我们特别关注语义压缩的好处。我们将语义信息称为从“意义”基础数据中学习到的一系列格式良好的符号，这些符号必须在接收者处进行解释。这需要一个基于知识库的推理单元（这里是人工的）：特定应用程序的符号知识表示。因此，我们提出并详细介绍了一种新的架构，该架构支持语义符号的表示学习，以实现有效的语义通信。我们首先讨论了理论方面，并成功地设计了目标函数，这有助于学习有效的语义编码器和解码器。最后，我们对文本传输的情况给出了有希望的数值结果，特别是当发送方和接收方使用不同的语言时。 摘要：Recently, semantic communications are envisioned as a key enabler of future 6G networks. Back to Shannon's information theory, the goal of communication has long been to guarantee the correct reception of transmitted messages irrespective of their meaning. However, in general, whenever communication occurs to convey a meaning, what matters is the receiver's understanding of the transmitted message and not necessarily its correct reconstruction. Hence, semantic communications introduce a new paradigm: transmitting only relevant information sufficient for the receiver to capture the meaning intended can save significant communication bandwidth. Thus, this work explores the opportunity offered by semantic communications for beyond 5G networks. In particular, we focus on the benefit of semantic compression. We refer to semantic message as a sequence of well-formed symbols learned from the "meaning" underlying data, which have to be interpreted at the receiver. This requires a reasoning unit, here artificial, on a knowledge base: a symbolic knowledge representation of the specific application. Therefore, we present and detail a novel architecture that enables representation learning of semantic symbols for effective semantic communications. We first discuss theoretical aspects and successfully design objective functions, which help learn effective semantic encoders and decoders. Eventually, we show promising numerical results for the scenario of text transmission, especially when the sender and receiver speak different languages.

【20】 Adversarial Attacks on ML Defense Models Competition 标题：ML防御模型大赛的对抗性攻击 链接：https://arxiv.org/abs/2110.08042

作者：Yinpeng Dong,Qi-An Fu,Xiao Yang,Wenzhao Xiang,Tianyu Pang,Hang Su,Jun Zhu,Jiayu Tang,Yuefeng Chen,XiaoFeng Mao,Yuan He,Hui Xue,Chao Li,Ye Liu,Qilong Zhang,Lianli Gao,Yunrui Yu,Xitong Gao,Zhe Zhao,Daquan Lin,Jiadong Lin,Chuanbiao Song,Zihao Wang,Zhennan Wu,Yang Guo,Jiequan Cui,Xiaogang Xu,Pengguang Chen 机构： Tsinghua University, Alibaba Group, RealAI, Shanghai Jiao Tong University, University of Electronic Science and Technology of China, University of Macau, Chinese Academy of Sciences, ShanghaiTech University, Huazhong University of Science and Technology 备注：Competition Report 摘要：由于深层神经网络（DNN）对对抗性示例的脆弱性，近年来提出了大量防御技术来缓解这一问题。然而，不完整或不正确的稳健性评估通常会阻碍建立更稳健模型的进程。为了加速对图像分类中当前防御模型的对抗鲁棒性进行可靠评估的研究，清华大学蔡尔集团和阿里巴巴安全集团组织了本次竞赛，并举办了CVPR 2021对抗性机器学习研讨会(https://aisecure-workshop.github.io/amlcvpr2021/). 本次竞赛的目的是激发新的攻击算法，以便更有效、更可靠地评估对手的鲁棒性。鼓励参与者开发更强大的白盒攻击算法，以发现不同防御的最坏情况鲁棒性。本次比赛在对抗性稳健性评估平台ARES上进行(https://github.com/thu-ml/ares)，并在天池平台上举行(https://tianchi.aliyun.com/competition/entrance/531847/introduction)作为AI安全挑战者计划系列之一。比赛结束后，我们总结了结果，并在https://ml.cs.tsinghua.edu.cn/ares-bench/，允许用户上传对抗性攻击算法和防御模型以进行评估。 摘要：Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years. However, the progress of building more robust models is usually hampered by the incomplete or incorrect robustness evaluation. To accelerate the research on reliable evaluation of adversarial robustness of the current defense models in image classification, the TSAIL group at Tsinghua University and the Alibaba Security group organized this competition along with a CVPR 2021 workshop on adversarial machine learning (https://aisecure-workshop.github.io/amlcvpr2021/). The purpose of this competition is to motivate novel attack algorithms to evaluate adversarial robustness more effectively and reliably. The participants were encouraged to develop stronger white-box attack algorithms to find the worst-case robustness of different defenses. This competition was conducted on an adversarial robustness evaluation platform -- ARES (https://github.com/thu-ml/ares), and is held on the TianChi platform (https://tianchi.aliyun.com/competition/entrance/531847/introduction) as one of the series of AI Security Challengers Program. After the competition, we summarized the results and established a new adversarial robustness benchmark at https://ml.cs.tsinghua.edu.cn/ares-bench/, which allows users to upload adversarial attack algorithms and defense models for evaluation.

【21】 Tensor-to-Image: Image-to-Image Translation with Vision Transformers 标题：张量到图像：使用视觉变换器进行图像到图像的转换 链接：https://arxiv.org/abs/2110.08037

作者：Yiğit Gündüç 摘要：Transformer自首次推出以来就受到了广泛的关注，并有着广泛的应用。Transformer开始接管深度学习的所有领域，视觉Transformer论文也证明了它们可以用于计算机视觉任务。在本文中，我们使用了一个基于视觉转换器的自定义设计模型，即张量到图像，用于图像到图像的转换。在自我关注的帮助下，我们的模型能够在不做任何修改的情况下推广并应用于不同的问题。 摘要：Transformers gain huge attention since they are first introduced and have a wide range of applications. Transformers start to take over all areas of deep learning and the Vision transformers paper also proved that they can be used for computer vision tasks. In this paper, we utilized a vision transformer-based custom-designed model, tensor-to-image, for the image to image translation. With the help of self-attention, our model was able to generalize and apply to different problems without a single modification.

【22】 Generating Natural Language Adversarial Examples through An Improved Beam Search Algorithm 标题：一种改进的波束搜索算法生成自然语言对抗性实例 链接：https://arxiv.org/abs/2110.08036

作者：Tengfei Zhao,Zhaocheng Ge,Hanping Hu,Dingmeng Shi 机构： School of Artifcial Intelligence and Automation, Huazhong University of Science and Technology, Key Laboratory of Image Information Processing and Intelligent Control, Ministry of Education 备注：9 pages, 4 figures 摘要：近年来，文本领域的对抗性攻击研究引起了人们的广泛关注，并提出了许多攻击成功率较高的方法。然而，这些攻击方法效率低下，因为它们在制作文本对抗性示例时需要对受害者模型进行大量查询。本文提出了一种新的攻击模型，其攻击成功率超过了基准攻击方法，但更重要的是，其攻击效率远远高于基准攻击方法。通过在四个基准数据集上攻击WordCNN、LSTM、BiLSTM和BERT，对新方法进行了实证评估。例如，在IMDB上攻击BERT和BiLSTM时，它的攻击成功率比最先进的方法高100%，但对受害者模型的查询数量仅为最先进方法的1/4和1/6.5。进一步的实验表明，该方法在生成的对抗性示例上具有良好的可移植性。 摘要：The research of adversarial attacks in the text domain attracts many interests in the last few years, and many methods with a high attack success rate have been proposed. However, these attack methods are inefficient as they require lots of queries for the victim model when crafting text adversarial examples. In this paper, a novel attack model is proposed, its attack success rate surpasses the benchmark attack methods, but more importantly, its attack efficiency is much higher than the benchmark attack methods. The novel method is empirically evaluated by attacking WordCNN, LSTM, BiLSTM, and BERT on four benchmark datasets. For instance, it achieves a 100\% attack success rate higher than the state-of-the-art method when attacking BERT and BiLSTM on IMDB, but the number of queries for the victim models only is 1/4 and 1/6.5 of the state-of-the-art method, respectively. Also, further experiments show the novel method has a good transferability on the generated adversarial examples.

【23】 A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead 标题：知识图谱构建技术现状及面临的挑战 链接：https://arxiv.org/abs/2110.08012

作者：Ali Hur,Naeem Janjua,Mohiuddin Ahmed 机构：Computer Science Department, Edith Cowan University, Perth, Australia 摘要：全球数据圈正在快速增长，预计到20251年将达到175 Zettabytes。但是，大多数内容是非结构化的，机器无法理解。将这些数据结构化为知识图可以实现多种智能应用，如深度问答、推荐系统、语义搜索等。知识图是一种新兴技术，它允许逻辑推理，并利用内容和上下文揭示新的见解。因此，它提供了必要的语法和推理语义，使机器能够解决复杂的医疗、安全、金融机构、经济和业务问题。因此，企业正在努力构建和维护知识图，以支持各种下游应用程序。手动方法太贵了。自动化方案可以将构建知识图的成本降低15-250倍。本文评述了自动生成接近人类素质的知识图的最先进的自动化技术。此外，它还强调了交付高质量知识图表需要解决的不同研究问题 摘要：Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251 . However, most of the content is unstructured and is not understandable by machines. Structuring this data into a knowledge graph enables multitudes of intelligent applications such as deep question answering, recommendation systems, semantic search, etc. The knowledge graph is an emerging technology that allows logical reasoning and uncovers new insights using content along with the context. Thereby, it provides necessary syntax and reasoning semantics that enable machines to solve complex healthcare, security, financial institutions, economics, and business problems. As an outcome, enterprises are putting their effort into constructing and maintaining knowledge graphs to support various downstream applications. Manual approaches are too expensive. Automated schemes can reduce the cost of building knowledge graphs up to 15-250 times. This paper critiques state-of-the-art automated techniques to produce knowledge graphs of near-human quality autonomously. Additionally, it highlights different research issues that need to be addressed to deliver high-quality knowledge graphs

【24】 A Broad-persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments 标题：机器人环境中深度交互式强化学习的广域持久建议方法 链接：https://arxiv.org/abs/2110.08003

作者：Hung Son Nguyen,Francisco Cruz,Richard Dazeley 机构： Deep ReinforcementTheauthorsarewiththeSchoolofInformationTechnology, DeakinUniversity 备注：10 pages 摘要：深度强化学习（deepreinforcionlearning，DeepRL）方法已广泛应用于机器人学中，用于学习环境和自主获取行为。深度互动强化学习（DeepIRL）包括来自外部训练师或专家的互动反馈，提供建议，帮助学习者选择行动以加快学习过程。然而，目前的研究仅限于仅对代理当前状态提供可操作建议的交互。此外，在一次使用后，代理会丢弃该信息，从而导致在相同状态下重复进程以进行重新访问。在本文中，我们提出了广泛的持久性建议（BPA），这是一种广泛的持久性建议方法，它保留并重用处理过的信息。它不仅帮助训练师提供与类似状态相关的更一般的建议，而不仅仅是当前状态，而且还允许代理加快学习过程。我们在两个连续的机器人场景中测试了所提出的方法，即手推车杆平衡任务和模拟机器人导航任务。所获得的结果表明，与DeepIRL方法相比，使用BPA的代理的性能有所提高，同时保持了训练师所需的交互次数。 摘要：Deep Reinforcement Learning (DeepRL) methods have been widely used in robotics to learn about the environment and acquire behaviors autonomously. Deep Interactive Reinforcement Learning (DeepIRL) includes interactive feedback from an external trainer or expert giving advice to help learners choosing actions to speed up the learning process. However, current research has been limited to interactions that offer actionable advice to only the current state of the agent. Additionally, the information is discarded by the agent after a single use that causes a duplicate process at the same state for a revisit. In this paper, we present Broad-persistent Advising (BPA), a broad-persistent advising approach that retains and reuses the processed information. It not only helps trainers to give more general advice relevant to similar states instead of only the current state but also allows the agent to speed up the learning process. We test the proposed approach in two continuous robotic scenarios, namely, a cart pole balancing task and a simulated robot navigation task. The obtained results show that the performance of the agent using BPA improves while keeping the number of interactions required for the trainer in comparison to the DeepIRL approach.

【25】 NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem 标题：NeuroLKH：深度学习模型与Lin-Kernighan-Helsagn启发式相结合求解旅行商问题 链接：https://arxiv.org/abs/2110.07983

作者：Liang Xin,Wen Song,Zhiguang Cao,Jie Zhang 机构：Nanyang Technological University, Shandong Unviersity, Qingdao, China, Singapore Institute of Manufacturing Technology 备注：Accepted at NeurIPS 2021 摘要：我们提出了一种新的算法NeuroLKH，它将深度学习和强大的传统启发式Lin-Kernighan-Helsgaun（LKH）相结合来解决旅行商问题。具体地说，我们训练了一个稀疏图网络（SGN），其中边分数的有监督学习和节点惩罚的无监督学习对提高LKH的性能至关重要。基于SGN的输出，Neurikh创建边缘候选集并变换边缘距离，以指导LKH的搜索过程。大量的实验有力地证明，通过对一个模型进行大范围的问题规模训练，NeuroKH显著优于LKH，并能很好地推广到更大的规模。此外，我们还证明了NeuroKH可以应用于其他路径问题，如容量限制车辆路径问题（CVRP）、取货和交货问题（PDP）和带时间窗的CVRP（CVRPTW）。 摘要：We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to much larger sizes. Also, we show that NeuroLKH can be applied to other routing problems such as Capacitated Vehicle Routing Problem (CVRP), Pickup and Delivery Problem (PDP), and CVRP with Time Windows (CVRPTW).

【26】 Reappraising Domain Generalization in Neural Networks 标题：神经网络中领域泛化的再评价 链接：https://arxiv.org/abs/2110.07981

作者：Sarath Sivaprasad,Akshay Goindani,Vaibhav Garg,Vineet Gandhi 机构：Kohli Centre on Intelligent Systems, IIIT Hyderabad, TCS Research, Pune 摘要：机器学习算法的领域泛化（DG）定义为从多个训练分布中学习领域不可知假设的能力，该假设从一个不可见的领域泛化到数据上。DG在具有明显特征的目标域具有稀疏训练数据的场景中至关重要。根据最近的工作{gullajani2020search}，我们发现直接的经验风险最小化（ERM）基线始终优于现有的DG方法。我们目前的研究表明，主干网、数据增强和优化算法的选择掩盖了现有技术中探索的许多技巧和交易。我们的工作使四种流行的DG数据集达到了新的技术水平，大大超过了以前的方法。此外，作为一个关键贡献，我们提出了一个类DG公式，其中对于每个类，我们随机选择一个域，并将其保留在一边进行测试。我们认为，这种基准测试更接近人类学习，并且与现实场景相关。我们在DomainBed上对类DG进行了全面的基准测试，并提出了一种结合ERM和反向梯度的方法，以获得最先进的结果。令我们惊讶的是，尽管在训练期间接触到了所有领域，但分类DG比传统DG评估更具挑战性，并激发了对DG问题更根本的反思。 摘要：Domain generalization (DG) of machine learning algorithms is defined as their ability to learn a domain agnostic hypothesis from multiple training distributions, which generalizes onto data from an unseen domain. DG is vital in scenarios where the target domain with distinct characteristics has sparse data for training. Aligning with recent work~\cite{gulrajani2020search}, we find that a straightforward Empirical Risk Minimization (ERM) baseline consistently outperforms existing DG methods. We present ablation studies indicating that the choice of backbone, data augmentation, and optimization algorithms overshadows the many tricks and trades explored in the prior art. Our work leads to a new state of the art on the four popular DG datasets, surpassing previous methods by large margins. Furthermore, as a key contribution, we propose a classwise-DG formulation, where for each class, we randomly select one of the domains and keep it aside for testing. We argue that this benchmarking is closer to human learning and relevant in real-world scenarios. We comprehensively benchmark classwise-DG on the DomainBed and propose a method combining ERM and reverse gradients to achieve the state-of-the-art results. To our surprise, despite being exposed to all domains during training, the classwise DG is more challenging than traditional DG evaluation and motivates more fundamental rethinking on the problem of DG.

【27】 Estimation and Prediction of Deterministic Human Intent Signal to augment Haptic Glove aided Control of Robotic Hand 标题：增加机械手触觉手套辅助控制的确定性人意图信号估计与预测 链接：https://arxiv.org/abs/2110.07953

作者：Rajesh Kumar,Pimmy Gandotra,Brejesh Lall,Arzad A. Kherani,Sudipto Mukherjee 摘要：本文主要研究基于触觉手套（HG）的机械手（RH）手部操纵控制。提出了一种控制算法，允许RH重新定位目标姿势。HG和RH的运动信号都是高维的。RH运动学通常不同于HG运动学。这两个装置的运动学变化，加上人手运动学信息不完整，导致难以将HG的高维运动信号直接映射到RH。因此，提出了一种从高维汞运动信号中估计人类意图并在右侧重建信号以确保目标重新定位的方法。研究还表明，人手运动信号合成的滞后加上RH的控制延迟，导致需要预测人的意图信号。然后，提出了一种递归神经网络（RNN）来提前预测人类意图信号。 摘要：The paper focuses on Haptic Glove (HG) based control of a Robotic Hand (RH) executing in-hand manipulation. A control algorithm is presented to allow the RH relocate the object held to a goal pose. The motion signals for both the HG and the RH are high dimensional. The RH kinematics is usually different from the HG kinematics. The variability of kinematics of the two devices, added with the incomplete information about the human hand kinematics result in difficulty in direct mapping of the high dimensional motion signal of the HG to the RH. Hence, a method is proposed to estimate the human intent from the high dimensional HG motion signal and reconstruct the signal at the RH to ensure object relocation. It is also shown that the lag in synthesis of the motion signal of the human hand added with the control latency of the RH leads to a requirement of the prediction of the human intent signal. Then, a recurrent neural network (RNN) is proposed to predict the human intent signal ahead of time.

【28】 Value Penalized Q-Learning for Recommender Systems 标题：价值惩罚Q-学习在推荐系统中的应用 链接：https://arxiv.org/abs/2110.07923

作者：Chengqian Gao,Ke Xu,Peilin Zhao 机构： Shenzhen International Graduate School, Tsinghua University, Tencent AI Lab 备注：An offline RL algorithm for recommender systems, 10 Pages 摘要：将强化学习（RL）扩展到推荐系统（RS）是有希望的，因为最大化RL代理的预期累积回报符合RS的目标，即提高客户的长期满意度。实现这一目标的关键方法是离线RL，其目的是从记录的数据中学习策略。然而，商业RS中的高维动作空间和非平稳动态加剧了分布转移问题，使得将离线RL方法应用于RS具有挑战性。为了缓解从静态轨迹提取RL策略时的动作分布转移问题，我们提出了值惩罚Q学习（VPQ），一种基于不确定性的离线RL算法。它通过不确定性感知权重惩罚回归目标中不稳定的Q值，无需估计行为策略，适用于具有大量项目的RS。我们从Q-函数集合的方差推导惩罚权重。为了缓解测试时的分布转移问题，我们进一步引入了critic框架，将所提出的方法与经典RS模型相结合。在两个真实数据集上进行的大量实验表明，该方法可以作为现有RS模型的增益插件。 摘要：Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, i.e., improving customers' long-term satisfaction. A key approach to this goal is offline RL, which aims to learn policies from logged data. However, the high-dimensional action space and the non-stationary dynamics in commercial RS intensify distributional shift issues, making it challenging to apply offline RL methods to RS. To alleviate the action distribution shift problem in extracting RL policy from static trajectories, we propose Value Penalized Q-learning (VPQ), an uncertainty-based offline RL algorithm. It penalizes the unstable Q-values in the regression target by uncertainty-aware weights, without the need to estimate the behavior policy, suitable for RS with a large number of items. We derive the penalty weights from the variances across an ensemble of Q-functions. To alleviate distributional shift issues at test time, we further introduce the critic framework to integrate the proposed method with classic RS models. Extensive experiments conducted on two real-world datasets show that the proposed method could serve as a gain plugin for existing RS models.

【29】 SaLinA: Sequential Learning of Agents 标题：Salina：Agent的顺序学习 链接：https://arxiv.org/abs/2110.07910

作者：Ludovic Denoyer,Alfredo de la Fuente,Song Duong,Jean-Baptiste Gaya,Pierre-Alexandre Kamienny,Daniel H. Thompson 机构：Facebook 摘要：SaLinA是一个简单的库，使实现复杂的顺序学习模型变得容易，包括强化学习算法。它是PyTorch的一个扩展：PyTorch用户可以在几分钟内理解用\SALINA{}编码的算法，并且很容易修改。此外，SaLinA在训练和测试时自然使用多个CPU和GPU，因此非常适合大规模训练用例。与现有的RL库相比，SaLinA具有非常低的采用成本，并捕获了大量的设置（基于模型的RL、批RL、层次RL、多代理RL等）。但是SaLinA不仅仅针对RL从业者，它的目标是为任何深度学习程序员提供顺序学习能力。 摘要：SaLinA is a simple library that makes implementing complex sequential learning models easy, including reinforcement learning algorithms. It is built as an extension of PyTorch: algorithms coded with \SALINA{} can be understood in few minutes by PyTorch users and modified easily. Moreover, SaLinA naturally works with multiple CPUs and GPUs at train and test time, thus being a good fit for the large-scale training use cases. In comparison to existing RL libraries, SaLinA has a very low adoption cost and capture a large variety of settings (model-based RL, batch RL, hierarchical RL, multi-agent RL, etc.). But SaLinA does not only target RL practitioners, it aims at providing sequential learning capabilities to any deep learning programmer.

【30】 Towards Better Plasticity-Stability Trade-off in Incremental Learning: A simple Linear Connector 标题：在增量学习中寻求更好的塑性稳定性权衡：一种简单的线性连接器 链接：https://arxiv.org/abs/2110.07905

作者：Guoliang Lin,Hanglu Chu,Hanjiang Lai 机构： Hanjiang Lai 3 1Sun Yat-Sen university, cn 2South China Normal University, cn 3Sun Yat-Sen university 摘要：可塑性-稳定性困境是增量学习的一个主要问题，可塑性指学习新知识的能力，稳定性指保留以前任务的知识。由于缺乏以前任务的训练样本，很难平衡可塑性和稳定性。例如，最近的零空间投影方法（如Adam NSCL）在保留以前的知识方面表现出了良好的性能，而这种强投影也会导致当前任务的性能下降。为了实现更好的塑性稳定性权衡，本文中，我们证明了两个独立优化的网络最优值的简单平均，过去任务的零空间投影和当前任务的简单SGD，可以在保留已学知识和赋予学习新任务的足够灵活性之间实现有意义的平衡。这种简单的线性连接器也为我们提供了一种新的视角和技术来控制塑性和稳定性之间的平衡。我们在几个基准数据集上对所提出的方法进行了评估。结果表明，我们的简单方法可以实现显著的改进，并且在过去和当前的任务中都表现良好。简言之，我们的方法是一种非常简单的方法，实现了更好的平衡模型。 摘要：Plasticity-stability dilemma is a main problem for incremental learning, with plasticity referring to the ability to learn new knowledge, and stability retaining the knowledge of previous tasks. Due to the lack of training samples from previous tasks, it is hard to balance the plasticity and stability. For example, the recent null-space projection methods (e.g., Adam-NSCL) have shown promising performance on preserving previous knowledge, while such strong projection also causes the performance degradation of the current task. To achieve better plasticity-stability trade-off, in this paper, we show that a simple averaging of two independently optimized optima of networks, null-space projection for past tasks and simple SGD for the current task, can attain a meaningful balance between preserving already learned knowledge and granting sufficient flexibility for learning a new task. This simple linear connector also provides us a new perspective and technology to control the trade-off between plasticity and stability. We evaluate the proposed method on several benchmark datasets. The results indicate our simple method can achieve notable improvement, and perform well on both the past and current tasks. In short, our method is an extremely simple approach and achieves a better balance model.

【31】 Certainty Modeling of a Decision Support System for Mobile Monitoring of Exercise induced Respiratory Conditions 标题：运动性呼吸状况移动监测决策支持系统确定性建模 链接：https://arxiv.org/abs/2110.07898

作者：Chinazunwa Uwaoma,Gunjan. Mansingh 机构：The University of the West Indies, Jamaica, Gunjan Mansingh 摘要：近年来，移动医疗系统通过使患者能够积极参与他们的健康，并通过方便获得医疗专业人员，显著改善了医疗保健部门。然而，这些移动系统的有效运行需要以决策支持系统（DSS）的形式实施高水平的智能和专业知识。然而，由于向推理模型提供的信息的动态性和不完整性，实现中的常见挑战包括泛化和可靠性。在这篇论文中，我们提出使用ad hoc移动决策支持系统来监测和检测由剧烈体力消耗引起的呼吸窘迫的触发因素和早期症状。重点研究了确定性理论在移动监控系统不精确推理建模中的应用。目的是开发一种移动工具，帮助患者管理病情，并提供客观的临床数据，以帮助医生筛查、诊断和治疗呼吸道疾病。我们提出了建议的模型架构，然后描述了临床环境中的应用场景。我们还展示了系统的一个方面的实现，使患者能够自我管理自己的病情。 摘要：Mobile health systems in recent times, have notably improved the healthcare sector by empowering patients to actively participate in their health, and by facilitating access to healthcare professionals. Effective operation of these mobile systems nonetheless, requires high level of intelligence and expertise implemented in the form of decision support systems (DSS). However, common challenges in the implementation include generalization and reliability, due to the dynamics and incompleteness of information presented to the inference models. In this paper, we advance the use of ad hoc mobile decision support system to monitor and detect triggers and early symptoms of respiratory distress provoked by strenuous physical exertion. The focus is on the application of certainty theory to model inexact reasoning by the mobile monitoring system. The aim is to develop a mobile tool to assist patients in managing their conditions, and to provide objective clinical data to aid physicians in the screening, diagnosis, and treatment of the respiratory ailments. We present the proposed model architecture and then describe an application scenario in a clinical setting. We also show implementation of an aspect of the system that enables patients in the self-management of their conditions.

【32】 A Machine Learning Approach for Delineating Similar Sound Symptoms of Respiratory Conditions on a Smartphone 标题：一种在智能手机上描绘类似呼吸状况声音症状的机器学习方法 链接：https://arxiv.org/abs/2110.07895

作者：Chinazunwa Uwaoma,Gunjan Mansingh 机构： Department of Computing, The University of West Indies, Kingston , Jamaica 摘要：呼吸音症状的临床特征和解释仍然是一个挑战，因为在医学诊断中听诊时表现出的音频特性相似。对这些声音的误解和混淆，以及相关疾病的共病病例，特别是运动诱发的呼吸条件；导致病情诊断和治疗不足。虽然有几项研究提出了计算机化系统来客观分类和评估这些声音，但大多数算法都运行在桌面和后端系统上。在本研究中，我们利用现代智能手机改进的计算和存储能力，使用机器学习算法（即：随机森林（RF）、支持向量机（SVM）和k近邻（k-NN）来区分呼吸音症状。这些分类器在手机上的显著性能表明，智能手机是在实时场景中识别和区分呼吸道症状的替代工具。此外，机器学习过程提供的客观临床数据可以帮助医生在门诊护理期间对患者进行筛查和治疗，因为在门诊护理期间，可能无法随时获得专门的医疗设备。 摘要：Clinical characterization and interpretation of respiratory sound symptoms have remained a challenge due to the similarities in the audio properties that manifest during auscultation in medical diagnosis. The misinterpretation and conflation of these sounds coupled with the comorbidity cases of the associated ailments particularly, exercised-induced respiratory conditions; result in the under-diagnosis and under-treatment of the conditions. Though several studies have proposed computerized systems for objective classification and evaluation of these sounds, most of the algorithms run on desktop and backend systems. In this study, we leverage the improved computational and storage capabilities of modern smartphones to distinguish the respiratory sound symptoms using machine learning algorithms namely: Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbour (k-NN). The appreciable performance of these classifiers on a mobile phone shows smartphone as an alternate tool for recognition and discrimination of respiratory symptoms in real-time scenarios. Further, the objective clinical data provided by the machine learning process could aid physicians in the screening and treatment of a patient during ambulatory care where specialized medical devices may not be readily available.

【33】 Role Similarity Metric Based on Spanning Rooted Forest 标题：基于生成根林的角色相似度度量 链接：https://arxiv.org/abs/2110.07872

作者：Qi Bao,Zhongzhi Zhang 机构：Fudan University, Shanghai, China 备注：10 pages, 5 figures 摘要：结构节点相似性作为网络分析中的一个基本问题，受到了学术界的广泛关注，并得到了广泛的应用。在这些提出的结构节点相似性度量中，角色相似性因满足包括自同构构象在内的若干公理性质而突出。由于高时间和空间成本，现有的角色相似性度量无法处理大型真实网络上的top-k查询。在本文中，我们提出了一种新的角色相似性度量，即\textsf{ForestSim}。我们证明了\textsf{ForestSim}是一个可容许的角色相似性度量，并设计了相应的top-k相似性搜索算法，即\textsf{ForestSimSearch}，一旦预计算完成，它能够在$O（k）$时间内处理top-k查询。此外，我们通过使用快速近似算法计算森林矩阵的对角项来加速预计算，从而将预计算的时间和空间复杂度分别降低到$O（\epsilon^{-2}m\log^5{n}\log{\frac{1}{\epsilon}}）和$O（m\log^3{n}）。最后，我们在26个真实网络上进行了广泛的实验。结果表明\textsf{ForestSim}在百万规模的网络上有效工作，并达到与最新方法相当的性能。 摘要：As a fundamental issue in network analysis, structural node similarity has received much attention in academia and is adopted in a wide range of applications. Among these proposed structural node similarity measures, role similarity stands out because of satisfying several axiomatic properties including automorphism conformation. Existing role similarity metrics cannot handle top-k queries on large real-world networks due to the high time and space cost. In this paper, we propose a new role similarity metric, namely \textsf{ForestSim}. We prove that \textsf{ForestSim} is an admissible role similarity metric and devise the corresponding top-k similarity search algorithm, namely \textsf{ForestSimSearch}, which is able to process a top-k query in $O(k)$ time once the precomputation is finished. Moreover, we speed up the precomputation by using a fast approximate algorithm to compute the diagonal entries of the forest matrix, which reduces the time and space complexity of the precomputation to $O(\epsilon^{-2}m\log^5{n}\log{\frac{1}{\epsilon}})$ and $O(m\log^3{n})$, respectively. Finally, we conduct extensive experiments on 26 real-world networks. The results show that \textsf{ForestSim} works efficiently on million-scale networks and achieves comparable performance to the state-of-art methods.

【34】 Exploring Low-dimensional Intrinsic Task Subspace via Prompt Tuning 标题：基于提示调优的低维固有任务子空间探索 链接：https://arxiv.org/abs/2110.07867

作者：Yujia Qin,Xiaozhi Wang,Yusheng Su,Yankai Lin,Ning Ding,Zhiyuan Liu,Juanzi Li,Lei Hou,Peng Li,Maosong Sun,Jie Zhou 机构：Department of Computer Science and Technology, Tsinghua University, Beijing, China, Pattern Recognition Center, WeChat AI, Tencent Inc. 摘要：预训练语言模型（PLM）如何学习通用表示并有效地适应表面上差异很大的广泛NLP任务？在这项工作中，我们经验地发现证据表明，PLMs对各种任务的适应可以被重新参数化为只优化公共低维内在任务子空间中的几个自由参数，这可能有助于我们理解为什么PLMs可以容易地适应具有小规模数据的各种NLP任务。具体来说，为了找到这样一个子空间并检验其普遍性，我们借助于最近成功的提示调整，将多个NLP任务的软提示分解为同一个低维非线性子空间，然后我们学习仅通过调整子空间中的参数使PLM适应看不见的任务或数据。我们将此管道称为内部提示调优（IPT）。在实验中，我们研究了不同的Few-ShotNLP任务，令人惊讶地发现，在一个包含100个随机任务的5维子空间中，仅调整5个自由参数，我们就可以分别恢复100个可见任务（使用不同的训练数据）和20个不可见任务的87%和65%的全提示调整性能，显示了所发现的内在任务子空间具有很强的泛化能力。除了作为一种分析工具外，IPT还可以进一步带来实际好处，例如提高快速调优稳定性。 摘要：How can pre-trained language models (PLMs) learn universal representations and effectively adapt to broad NLP tasks differing a lot superficially? In this work, we empirically find evidences indicating that the adaptations of PLMs to various tasks can be reparameterized as optimizing only a few free parameters in a common low-dimensional intrinsic task subspace, which may help us understand why PLMs could easily adapt to various NLP tasks with small-scale data. Specifically, to find such a subspace and examine its universality, we resort to the recent success of prompt tuning and decompose the soft prompts of multiple NLP tasks into the same low-dimensional nonlinear subspace, then we learn to adapt the PLM to unseen tasks or data by only tuning parameters in the subspace. We dub this pipeline as intrinsic prompt tuning (IPT). In experiments, we study diverse few-shot NLP tasks and surprisingly find that in a 5-dimensional subspace found with 100 random tasks, by only tuning 5 free parameters, we can recover 87% and 65% of the full prompt tuning performance for 100 seen tasks (using different training data) and 20 unseen tasks, respectively, showing great generalization ability of the found intrinsic task subspace. Besides being an analysis tool, IPT could further bring practical benefits, such as improving the prompt tuning stability.

【35】 Machine Learning Algorithms In User Authentication Schemes 标题：用户认证方案中的机器学习算法 链接：https://arxiv.org/abs/2110.07826

作者：Laura Pryor,Dr. Rushit Dave,Dr. Naeem Seliya,Dr. Evelyn R Sowells Boone 机构：Department of Computer Science, University of Wisconsin – Eau, Eau Claire, WI, USA, Dr. Jim Seliya, Dr. Evelyn Sowells Boone, North Carolina A&T State, Greensboro, NC, USA 摘要：在过去二十年中，公司创造的移动产品数量呈指数级增长。然而，尽管这些设备不断更新最新功能，但在过去二十年中，用于保护这些设备的安全措施相对保持不变。设备及其安全性之间的增长模式存在巨大差异，这使得越来越多的设备很容易被恶意用户渗透。在该领域先前工作的基础上，本研究着眼于涉及触摸动态和设备移动的用户认证方案中使用的不同机器学习算法。本研究旨在全面概述当前在涉及触摸动态和设备移动的用户认证模式中经常使用的不同机器学习算法的使用情况。本文将全面讨论其优点、局限性以及对未来工作的建议。 摘要：In the past two decades, the number of mobile products being created by companies has grown exponentially. However, although these devices are constantly being upgraded with the newest features, the security measures used to protect these devices has stayed relatively the same over the past two decades. The vast difference in growth patterns between devices and their security is opening up the risk for more and more devices to easily become infiltrated by nefarious users. Working off of previous work in the field, this study looks at the different Machine Learning algorithms used in user authentication schemes involving touch dynamics and device movement. This study aims to give a comprehensive overview of the current uses of different machine learning algorithms that are frequently used in user authentication schemas involving touch dynamics and device movement. The benefits, limitations, and suggestions for future work will be thoroughly discussed throughout this paper.

【36】 Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help? 标题：多语言神经机器翻译：语言层次能有所帮助吗？ 链接：https://arxiv.org/abs/2110.07816

作者：Fahimeh Saleh,Wray Buntine,Gholamreza Haffari,Lan Du 机构：Monash University 摘要：多语言神经机器翻译（MNMT）训练单一的NMT模型，支持多种语言之间的翻译，而不是为不同的语言训练单独的模型。通过利用来自多种语言的数据，学习单一模型可以增强低资源翻译。然而，MNMT模型的性能在很大程度上取决于训练中使用的语言类型，因为从不同语言中转移知识会由于负迁移而降低翻译性能。在本文中，我们提出了一种用于MNMT的分层知识提取（HKD）方法，该方法利用根据语言的类型特征和发展史生成的语言组来克服负迁移问题。HKD通过基于语言组的选择性知识提取机制生成一组多语言助教模型，然后以自适应方式从这些助教中提取最终的多语言模型。来自53种语言的TED数据集的实验结果表明，与强基线相比，我们的方法在避免MNMT中的负迁移效应方面是有效的，从而提高了翻译性能（平均BLEU分数约为1）。 摘要：Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages. Learning a single model can enhance the low-resource translation by leveraging data from multiple languages. However, the performance of an MNMT model is highly dependent on the type of languages used in training, as transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. In this paper, we propose a Hierarchical Knowledge Distillation (HKD) approach for MNMT which capitalises on language groups generated according to typological features and phylogeny of languages to overcome the issue of negative transfer. HKD generates a set of multilingual teacher-assistant models via a selective knowledge distillation mechanism based on the language groups, and then distils the ultimate multilingual model from those assistants in an adaptive way. Experimental results derived from the TED dataset with 53 languages demonstrate the effectiveness of our approach in avoiding the negative transfer effect in MNMT, leading to an improved translation performance (about 1 BLEU score on average) compared to strong baselines.

【37】 ContraQA: Question Answering under Contradicting Contexts 标题：ContraQA：矛盾语境下的问答 链接：https://arxiv.org/abs/2110.07803

作者：Liangming Pan,Wenhu Chen,Min-Yen Kan,William Yang Wang 机构：National University of Singapore, Singapore, University of California, Santa Barbara, CA, USA 备注：Technical report 摘要：随着宣传、新闻和社交媒体中虚假、不准确和误导性信息的增加，真实世界的问答（QA）系统面临着综合和推理矛盾信息以得出正确答案的挑战。这种紧迫性导致需要使QA系统对错误信息具有鲁棒性，这是一个以前未探讨过的话题。我们通过调查QA模型在混合了真实和虚假信息的矛盾情境下的行为来研究错误信息对QA模型的风险。我们为这个问题创建了第一个大规模数据集，即Contra QA，其中包含超过10000个人类编写的和模型生成的矛盾上下文对。实验表明，在错误信息带来的矛盾语境下，QA模型是脆弱的。为了防御这样的威胁，我们建立了一个错误信息感知的QA系统，作为一种对抗措施，它以联合方式集成了问答和错误信息检测。 摘要：With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over contradicting information to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the behavior of the QA model under contradicting contexts that are mixed with both real and fake information. We create the first large-scale dataset for this problem, namely Contra-QA, which contains over 10K human-written and model-generated contradicting pairs of contexts. Experiments show that QA models are vulnerable under contradicting contexts brought by misinformation. To defend against such a threat, we build a misinformation-aware QA system as a counter-measure that integrates question answering and misinformation detection in a joint fashion.

【38】 Occupancy Estimation from Thermal Images 标题：基于热像的入住率估算 链接：https://arxiv.org/abs/2110.07796

作者：Zishan Qin,Dipankar Chaki,Abdallah Lakhdari,Amani Abusafia,Athman Bouguettaya 机构： School of Computing, Australian National University, Canberra, Australia, School of Computer Science, University of Sydney, Australia 备注：4 pages, 2 figures. This is an accepted demo paper and to be published in the proceedings of 19th International Conference on Service Oriented Computing (ICSOC 2021) 摘要：我们提出了一个非侵入性的、保护隐私的智能环境占用率估计系统。该方案利用热图像检测给定区域内的人数。使用基于强度和基于运动的人体分割的概念设计占用率估计模型。利用差分捕捉器、连通元件标记、噪声滤波器和记忆传播的概念来估计占用数。我们使用一个真实的数据集来证明所提出的系统的有效性。 摘要：We propose a non-intrusive, and privacy-preserving occupancy estimation system for smart environments. The proposed scheme uses thermal images to detect the number of people in a given area. The occupancy estimation model is designed using the concepts of intensity-based and motion-based human segmentation. The notion of difference catcher, connected component labeling, noise filter, and memory propagation are utilized to estimate the occupancy number. We use a real dataset to demonstrate the effectiveness of the proposed system.

【39】 Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models 标题：使用深度学习模型从高级文本描述创建用户界面模型 链接：https://arxiv.org/abs/2110.07775

作者：Forrest Huang,Gang Li,Xin Zhou,John F. Canny,Yang Li 机构： University of California 摘要：用户界面（UI）的设计过程通常从阐明高层设计目标开始。然而，将这些高级设计目标转化为具体的设计模型需要大量的工作和UI设计专业知识。为了促进应用程序设计师和开发人员的这一过程，我们引入了三种深度学习技术，从描述高级设计目标的自然语言短语（例如，“弹出显示图像和其他选项”）创建低保真UI模型。特别是，我们提供了两种基于检索的方法和一种生成方法，以及前处理和后处理技术，以确保创建的UI模型的质量。我们定量和定性地比较和对比了每种方法在建议连贯、多样和相关的UI设计模型方面的能力。我们与15名专业UI设计师和实践者一起进一步评估这些方法，以了解每种方法的优缺点。设计师对这些方法在协助设计过程中的潜力做出了积极的回应。 摘要：The design process of user interfaces (UIs) often begins with articulating high-level design goals. Translating these high-level design goals into concrete design mock-ups, however, requires extensive effort and UI design expertise. To facilitate this process for app designers and developers, we introduce three deep-learning techniques to create low-fidelity UI mock-ups from a natural language phrase that describes the high-level design goal (e.g. "pop up displaying an image and other options"). In particular, we contribute two retrieval-based methods and one generative method, as well as pre-processing and post-processing techniques to ensure the quality of the created UI mock-ups. We quantitatively and qualitatively compare and contrast each method's ability in suggesting coherent, diverse and relevant UI design mock-ups. We further evaluate these methods with 15 professional UI designers and practitioners to understand each method's advantages and disadvantages. The designers responded positively to the potential of these methods for assisting the design process.

【40】 The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization 标题：神经数据路由器：Transformer中的自适应控制流改进了系统泛化 链接：https://arxiv.org/abs/2110.07732

作者：Róbert Csordás,Kazuki Irie,Jürgen Schmidhuber 机构：J¨urgen Schmidhuber, The Swiss AI Lab, IDSIA, University of Lugano (USI) & SUPSI, Lugano, Switzerland, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia 摘要：尽管在广泛的应用中取得了成功，但Transformer在系统化推广方面的成功有限。在算法任务的情况下，这种情况尤其令人沮丧，在这种情况下，他们往往无法找到直观的解决方案，在Transformer列表示的网格中的正确时间将相关信息路由到正确的节点/操作。为了便于学习有用的控制流，我们对Transformer结构提出了两个修改：复制门和几何注意。我们的新型神经数据路由器（NDR）在经典合成表查找任务上实现了100%的长度泛化精度，在简单算术任务上实现了近乎完美的精度，并且在计算深度上实现了一种新的ListOps泛化测试变体。NDR的注意力和门控模式往往可以解释为一种直观的神经路由形式。我们的代码是公开的。 摘要：Despite successes across a broad range of applications, Transformers have limited success in systematic generalization. The situation is especially frustrating in the case of algorithmic tasks, where they often fail to find intuitive solutions that route relevant information to the right node/operation at the right time in the grid represented by Transformer columns. To facilitate the learning of useful control flow, we propose two modifications to the Transformer architecture, copy gate and geometric attention. Our novel Neural Data Router (NDR) achieves 100% length generalization accuracy on the classic compositional table lookup task, as well as near-perfect accuracy on the simple arithmetic task and a new variant of ListOps testing for generalization across computational depth. NDR's attention and gating patterns tend to be interpretable as an intuitive form of neural routing. Our code is public.

【41】 An Independent Study of Reinforcement Learning and Autonomous Driving 标题：强化学习与自主驾驶的独立研究 链接：https://arxiv.org/abs/2110.07729

作者：Hanzhi Yang 机构：edu University of Michigan 备注：32 pages in total, 7 figures, 3 appendices, 5 tables 摘要：强化学习已成为近十年来最热门的学科之一。它已在机器人操作、自动驾驶、路径规划、计算机游戏等各个领域得到应用。在本项目期间，我们完成了三项任务。首先，我们研究了表格环境下的Q-学习算法，并将其成功应用于OpenAi健身房环境Taxi。其次，我们了解并实现了Cart-Pole环境下的deep-Q网络算法。第三，我们还研究了强化学习在自主驾驶中的应用及其与安全检查约束（安全控制器）的结合。我们使用高速公路健身房环境训练了一个粗糙的自主驾驶代理，并探讨了奖励函数等各种环境配置对代理训练性能的影响。 摘要：Reinforcement learning has become one of the most trending subjects in the recent decade. It has seen applications in various fields such as robot manipulations, autonomous driving, path planning, computer gaming, etc. We accomplished three tasks during the course of this project. Firstly, we studied the Q-learning algorithm for tabular environments and applied it successfully to an OpenAi Gym environment, Taxi. Secondly, we gained an understanding of and implemented the deep Q-network algorithm for Cart-Pole environment. Thirdly, we also studied the application of reinforcement learning in autonomous driving and its combination with safety check constraints (safety controllers). We trained a rough autonomous driving agent using highway-gym environment and explored the effects of various environment configurations like reward functions on the agent training performance.

【42】 The Sigma-Max System Induced from Randomness and Fuzziness 标题：由随机性和模糊性引出的Sigma-Max系统 链接：https://arxiv.org/abs/2110.07722

作者：Wei Mei,Ming Li,Yuanzeng Cheng,Limin Liu 机构：Electronic Engineering Department, Army Engineering University, Shijiazhuang, P.R. China 摘要：本文试图从随机性和模糊性中分别归纳出概率论（sigma系统）和可能性理论（max系统），期望通过这两种理论能够很好地建立早熟可能性理论。这一目标是通过解决三个公开的关键问题来实现的：a）缺乏对随机性和模糊性的明确数学定义；b） 缺乏对可能性的直观数学定义；c） 缺乏从直观定义中抽象概率/可能性公理化定义的程序。特别是，最后一个问题涉及到为什么采用“最大性”这一关键公理作为可能性度量的问题。利用定义良好的随机性和模糊性的特性，我们得出了一个重要结论，即“max”是适用于整个模糊事件空间的唯一但不严格的析取算子，是模糊特征提取的精确算子，它确保了max推理是一种精确机制。可以说，对可能性理论的基础上缺乏共识的长期问题得到了很好的解决，这将有助于在实践中更广泛地采用可能性理论，并促进两个概率论和可能性论的不确定性理论的交叉繁荣。 摘要：This paper managed to induce probability theory (sigma system) and possibility theory (max system) respectively from randomness and fuzziness, through which the premature theory of possibility is expected to be well founded. Such an objective is achieved by addressing three open key issues: a) the lack of clear mathematical definitions of randomness and fuzziness; b) the lack of intuitive mathematical definition of possibility; c) the lack of abstraction procedure of the axiomatic definitions of probability/possibility from their intuitive definitions. Especially, the last issue involves the question why the key axiom of "maxitivity" is adopted for possibility measure. By taking advantage of properties of the well-defined randomness and fuzziness, we derived the important conclusion that "max" is the only but un-strict disjunctive operator that is applicable across the fuzzy event space, and is an exact operator for fuzzy feature extraction that assures the max inference is an exact mechanism. It is fair to claim that the long-standing problem of lack of consensus to the foundation of possibility theory is well resolved, which would facilitate wider adoption of possibility theory in practice and promote cross prosperity of the two uncertainty theories of probability and possibility.

【43】 Certified Patch Robustness via Smoothed Vision Transformers 标题：通过平滑视觉转换器认证的补丁稳健性 链接：https://arxiv.org/abs/2110.07719

作者：Hadi Salman,Saachi Jain,Eric Wong,Aleksander Mądry 机构：MIT, Aleksander M ˛adry 摘要：认证补丁防御可以保证图像分类器对有界连续区域内的任意变化的鲁棒性。但是，目前，这种稳健性是以标准精度降低和推理时间变慢为代价的。我们演示了如何使用视觉变换器实现显著更好的认证面片鲁棒性，从而提高计算效率，并且不会导致标准精度的大幅下降。这些改进源于视觉转换器的固有能力，它可以优雅地处理大部分被遮掩的图像。我们的代码可在https://github.com/MadryLab/smoothed-vit. 摘要：Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region. But, currently, this robustness comes at a cost of degraded standard accuracies and slower inference times. We demonstrate how using vision transformers enables significantly better certified patch robustness that is also more computationally efficient and does not incur a substantial drop in standard accuracy. These improvements stem from the inherent ability of the vision transformer to gracefully handle largely masked images. Our code is available at https://github.com/MadryLab/smoothed-vit.

【44】 Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning 标题：深度人引导条件变分生成模型在城市规划自动化中的应用 链接：https://arxiv.org/abs/2110.07717

作者：Dongjie Wang,Kunpeng Liu,Pauline Johnson,Leilei Sun,Bowen Du,Yanjie Fu 机构：Department of Computer Science, University of Central Florida, Orlando, Department of Computer Science, Beihang University, Beijing 备注：ICDM2021 摘要：城市规划设计土地使用结构，有利于建设宜居、可持续、安全的社区。受图像生成的启发，深度城市规划旨在利用深度学习生成土地使用配置。然而，城市规划是一个复杂的过程。现有的研究通常忽略了规划中个性化的人的引导需求，以及规划生成中的空间层次结构。此外，缺乏大规模土地利用配置样本构成了数据稀疏性的挑战。本文研究了一种新的深度人文引导的城市规划方法，以共同解决上述挑战。具体地说，我们将问题转化为一个基于深度条件变分自动编码器的框架。在这个框架中，我们利用深度编码器-解码器设计来生成土地使用配置。为了捕获土地利用的空间层次结构，我们强制解码器生成功能区的粗粒度层和POI分布的细粒度层。为了整合人类指导，我们允许人类将他们需要的描述为文本，并将这些文本用作模型条件输入。为了减少训练数据的稀疏性，提高模型的鲁棒性，我们引入了一种变分高斯嵌入机制。它不仅使我们能够更好地逼近训练数据的嵌入空间分布，并对更大的人群进行抽样以克服稀疏性，而且在城市规划生成中增加了更多的概率随机性，以提高嵌入的多样性，从而提高鲁棒性。最后，我们进行了大量的实验来验证我们方法的增强性能。 摘要：Urban planning designs land-use configurations and can benefit building livable, sustainable, safe communities. Inspired by image generation, deep urban planning aims to leverage deep learning to generate land-use configurations. However, urban planning is a complex process. Existing studies usually ignore the need of personalized human guidance in planning, and spatial hierarchical structure in planning generation. Moreover, the lack of large-scale land-use configuration samples poses a data sparsity challenge. This paper studies a novel deep human guided urban planning method to jointly solve the above challenges. Specifically, we formulate the problem into a deep conditional variational autoencoder based framework. In this framework, we exploit the deep encoder-decoder design to generate land-use configurations. To capture the spatial hierarchy structure of land uses, we enforce the decoder to generate both the coarse-grained layer of functional zones, and the fine-grained layer of POI distributions. To integrate human guidance, we allow humans to describe what they need as texts and use these texts as a model condition input. To mitigate training data sparsity and improve model robustness, we introduce a variational Gaussian embedding mechanism. It not just allows us to better approximate the embedding space distribution of training data and sample a larger population to overcome sparsity, but also adds more probabilistic randomness into the urban planning generation to improve embedding diversity so as to improve robustness. Finally, we present extensive experiments to validate the enhanced performances of our method.

【45】 Semi-automated checking for regulatory compliance in e-Health 标题：电子健康中法规遵从性的半自动检查 链接：https://arxiv.org/abs/2110.07710

作者：Ilaria Angela Amantea,Livio Robaldo,Emilio Sulis,Guido Boella,Guido Governatori 机构：Computer Science Department, CIRSFID, SaToSS, Universita degli Studi di Torino, Alma Mater Studiorum, Universit´e du Luxembourg, Turin - Bologna, Italy, Luxembourg, Legal Innovation Lab Wales, University of Swansea, Swansea, Wales (UK), Turin, Italy, Data , CSIRO 摘要：每个业务流程的主要问题之一是遵守法律规则。这项工作提出了一种方法，以半自动化的方式检查业务流程的法规遵从性。我们特别分析了电子健康医院服务：家庭医院（HaH）服务。本文首先使用业务流程管理和符号（BPMN）标准语言对医院业务进行分析，然后在可撤销道义逻辑（DDL）中对欧洲通用数据保护条例（GDPR）的一些规则进行形式化。其目的是展示如何使用工具将业务的一组任务与要遵守的一组规则结合起来。 摘要：One of the main issues of every business process is to be compliant with legal rules. This work presents a methodology to check in a semi-automated way the regulatory compliance of a business process. We analyse an e-Health hospital service in particular: the Hospital at Home (HaH) service. The paper shows, at first, the analysis of the hospital business using the Business Process Management and Notation (BPMN) standard language, then, the formalization in Defeasible Deontic Logic (DDL) of some rules of the European General Data Protection Regulation (GDPR). The aim is to show how to combine a set of tasks of a business with a set of rules to be compliant with, using a tool.

【46】 Exposing Query Identification for Search Transparency 标题：公开查询标识以提高搜索透明度 链接：https://arxiv.org/abs/2110.07701

作者：Ruohan Li,Jianxiang Li,Bhaskar Mitra,Fernando Diaz,Asia J. Biega 机构：Carnegie Mellon University, Microsoft, United States, Microsoft, University College London, Canada, Microsoft Research, Max Planck Institute, for Security and Privacy, Germany 摘要：搜索系统控制对搜索者公开排名的内容。在许多情况下，创作者不仅重视内容的曝光，而且还重视对内容出现的特定搜索的理解。识别哪些查询在排名结果中公开给定的内容是一个重要的问题，但搜索透明度方面的挑战相对较少。公开查询有助于量化搜索偏差、隐私、数据保护、安全性和搜索引擎优化等各种问题。在给定系统中准确识别公开查询的计算代价很高，特别是在动态上下文中，如web搜索。为了寻求一个更轻量级的解决方案，我们通过改变查询和文档在两类搜索系统（密集双编码器模型和传统BM25模型）中的作用，探讨了近似公开查询标识（EQI）作为检索任务的可行性。然后，我们提出了如何通过检索嵌入空间上的度量学习来改进这种方法。我们进一步推导了一个评估指标来衡量公开查询排名的质量，并针对近似EQI的各个实际方面进行了实证分析。 摘要：Search systems control the exposure of ranked content to searchers. In many cases, creators value not only the exposure of their content but, moreover, an understanding of the specific searches where the content is surfaced. The problem of identifying which queries expose a given piece of content in the ranking results is an important and relatively under-explored search transparency challenge. Exposing queries are useful for quantifying various issues of search bias, privacy, data protection, security, and search engine optimization. Exact identification of exposing queries in a given system is computationally expensive, especially in dynamic contexts such as web search. In quest of a more lightweight solution, we explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems: dense dual-encoder models and traditional BM25 models. We then propose how this approach can be improved through metric learning over the retrieval embedding space. We further derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI.

【47】 Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units 标题：后见之明网络信用分配：离散随机单元网络中的有效信用分配 链接：https://arxiv.org/abs/2110.07700

作者：Kenny Young 机构：Department of Computing Science, University of Alberta, Edmonton, Canada 摘要：用离散随机变量训练神经网络是一个独特的挑战。反向传播不是直接适用的，在具有连续随机变量的网络中也没有使用重新参数化技巧。为了应对这一挑战，我们提出了后见网络信用分配（HNCA），一种新的离散随机单元网络学习算法。HNCA的工作方式是根据每个单位的产出对其网络中直系子女的影响程度，为每个单位分配学分。我们证明了与强化估计相比，HNCA产生方差减小的无偏梯度估计，而计算量与反向传播近似。我们首先在上下文bandit设置中应用HNCA来优化代理未知的奖励函数。在此背景下，我们实证证明HNCA显著优于Enstructure，表明我们的理论分析所暗示的方差减少是显著且有效的。然后，我们展示了如何扩展HNCA以优化随机单元网络输出的更一般的函数，其中函数是代理已知的。我们将此扩展版HNCA应用于训练离散变分自动编码器，并通过实验证明其优于其他强方法。我们相信，HNCA背后的思想有助于激发关于随机计算图中有效信用分配的新思路。 摘要：Training neural networks with discrete stochastic variables presents a unique challenge. Backpropagation is not directly applicable, nor are the reparameterization tricks used in networks with continuous stochastic variables. To address this challenge, we present Hindsight Network Credit Assignment (HNCA), a novel learning algorithm for networks of discrete stochastic units. HNCA works by assigning credit to each unit based on the degree to which its output influences its immediate children in the network. We prove that HNCA produces unbiased gradient estimates with reduced variance compared to the REINFORCE estimator, while the computational cost is similar to that of backpropagation. We first apply HNCA in a contextual bandit setting to optimize a reward function that is unknown to the agent. In this setting, we empirically demonstrate that HNCA significantly outperforms REINFORCE, indicating that the variance reduction implied by our theoretical analysis is significant and impactful. We then show how HNCA can be extended to optimize a more general function of the outputs of a network of stochastic units, where the function is known to the agent. We apply this extended version of HNCA to train a discrete variational auto-encoder and empirically show it compares favourably to other strong methods. We believe that the ideas underlying HNCA can help stimulate new ways of thinking about efficient credit assignment in stochastic compute graphs.

【48】 Safety-aware Policy Optimisation for Autonomous Racing 标题：自主赛车的安全感知策略优化 链接：https://arxiv.org/abs/2110.07699

作者：Bingqing Chen,Jonathan Francis,James Herman,Jean Oh,Eric Nyberg,Sylvia L. Herbert 机构：School of Computer Science, Carnegie Mellon University, Pittsburgh, PA , Human-Machine Collaboration, Bosch Research Pittsburgh, Pittsburgh, PA , Safe Autonomous Systems, University of California, San Diego, CA 备注：22 pages, 14 figures, 3 tables 摘要：为了适用于安全关键应用，如自动驾驶和辅助机器人，自动代理应在与其环境的整个交互过程中遵守安全约束。汉密尔顿-雅可比（HJ）可达性等方法不是通过收集样本（包括不安全样本）来学习安全性，而是使用系统动力学模型计算具有理论保证的安全集。然而，HJ可达性不能扩展到高维系统，其保证取决于模型的质量。在这项工作中，我们将HJ可达性理论注入到约束马尔可夫决策过程（CMDP）框架中，作为通过状态-动作对的无模型更新进行安全分析的控制理论方法。此外，我们证明HJ安全值可以直接在视觉环境中学习，这是迄今为止通过该方法研究的最高维度问题。我们在几个基准任务上评估了我们的方法，包括安全健身房和学会比赛（L2R），一个最近发布的高保真自主比赛环境。与其他受约束的RL基线相比，我们的方法明显减少了违反约束的情况，并在L2R基准任务上实现了新的最新成果。 摘要：To be viable for safety-critical applications, such as autonomous driving and assistive robotics, autonomous agents should adhere to safety constraints throughout the interactions with their environments. Instead of learning about safety by collecting samples, including unsafe ones, methods such as Hamilton-Jacobi (HJ) reachability compute safe sets with theoretical guarantees using models of the system dynamics. However, HJ reachability is not scalable to high-dimensional systems, and the guarantees hinge on the quality of the model. In this work, we inject HJ reachability theory into the constrained Markov decision process (CMDP) framework, as a control-theoretical approach for safety analysis via model-free updates on state-action pairs. Furthermore, we demonstrate that the HJ safety value can be learned directly on vision context, the highest-dimensional problem studied via the method to-date. We evaluate our method on several benchmark tasks, including Safety Gym and Learn-to-Race (L2R), a recently-released high-fidelity autonomous racing environment. Our approach has significantly fewer constraint violations in comparison to other constrained RL baselines, and achieve the new state-of-the-art results on the L2R benchmark task.

【49】 Making Document-Level Information Extraction Right for the Right Reasons 标题：有理有据地做好文档级信息抽取 链接：https://arxiv.org/abs/2110.07686

作者：Liyan Tang,Dhruv Rajan,Suyash Mohan,Abhijeet Pradhan,R. Nick Bryan,Greg Durrett 机构：The University of Texas at Austin, University of Pennsylvania, Galileo CDS Inc. 备注：9 pages (14 with references and appendix), 3 figures 摘要：文档级信息提取是一个灵活的框架，适用于信息不一定在一句话中本地化的应用程序。例如，放射学报告中诊断的关键特征可能没有明确说明，但可以从报告文本中推断出来。然而，文档级神经模型可以很容易地从无关信息中学习虚假的相关性。这项工作研究如何确保这些模型从复杂文本中做出正确的推断，并以可审计的方式做出这些推断：除了正确之外，这些模型是否“出于正确的原因”正确？我们使用特征归因技术在预测-选择-验证框架中进行事后证据提取实验。虽然这种基本方法可以提取合理的证据，但可以在训练期间通过少量的证据监督加以规范，这大大提高了提取证据的质量。我们在两个领域进行评估：一个脑MRI报告的小规模标记数据集和DocRED的大规模修改版本（Yao等人，2019年），并表明模型的合理性可以在不损失准确性的情况下得到提高。 摘要：Document-level information extraction is a flexible framework compatible with applications where information is not necessarily localized in a single sentence. For example, key features of a diagnosis in radiology a report may not be explicitly stated, but nevertheless can be inferred from the report's text. However, document-level neural models can easily learn spurious correlations from irrelevant information. This work studies how to ensure that these models make correct inferences from complex text and make those inferences in an auditable way: beyond just being right, are these models "right for the right reasons?" We experiment with post-hoc evidence extraction in a predict-select-verify framework using feature attribution techniques. While this basic approach can extract reasonable evidence, it can be regularized with small amounts of evidence supervision during training, which substantially improves the quality of extracted evidence. We evaluate on two domains: a small-scale labeled dataset of brain MRI reports and a large-scale modified version of DocRED (Yao et al., 2019) and show that models' plausibility can be improved with no loss in accuracy.

【50】 GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems 标题：GlobalWoZ：全球化MultiWoZ开发面向任务的多语言对话系统 链接：https://arxiv.org/abs/2110.07679

作者：Bosheng Ding,Junjie Hu,Lidong Bing,Sharifah Mahani Aljunied,Shafiq Joty,Luo Si,Chunyan Miao 机构：Nanyang Technological University, Singapore ,DAMO Academy, Alibaba Group, University of Wisconsin-Madison 摘要：任务导向对话（ToD）系统的许多最新进展都是由跨多个训练领域的可用注释数据推动的。在过去的几年中，有一种趋势是多语言ToD系统的数据管理，适用于服务于使用不同语言的人。然而，现有的多语言ToD数据集要么由于数据整理的高成本而对语言的覆盖范围有限，要么忽视了对话实体在使用这些语言的国家几乎不存在的事实。为了解决这些局限性，我们引入了一种新的数据管理方法来生成GlobalWoZ——一个大规模的多语言ToD数据集，它从一个英语ToD数据集全球化，用于三个未开发的用例。我们的方法基于翻译对话模板，并用目标语言国家的本地实体填充它们。我们发布了我们的数据集以及一组强大的基线，以鼓励研究如何为实际用例学习多语言ToD系统。 摘要：Much recent progress in task-oriented dialogue (ToD) systems has been driven by available annotation data across multiple domains for training. Over the last few years, there has been a move towards data curation for multilingual ToD systems that are applicable to serve people speaking different languages. However, existing multilingual ToD datasets either have a limited coverage of languages due to the high cost of data curation, or ignore the fact that dialogue entities barely exist in countries speaking these languages. To tackle these limitations, we introduce a novel data curation method that generates GlobalWoZ -- a large-scale multilingual ToD dataset globalized from an English ToD dataset for three unexplored use cases. Our method is based on translating dialogue templates and filling them with local entities in the target-language countries. We release our dataset as well as a set of strong baselines to encourage research on learning multilingual ToD systems for real use cases.

【51】 Interactive Analysis of CNN Robustness 标题：CNN稳健性的交互分析 链接：https://arxiv.org/abs/2110.07667

作者：Stefan Sietzen,Mathias Lechner,Judy Borowski,Ramin Hasani,Manuela Waldner 备注：Accepted at Pacific Graphics 2021 摘要：虽然卷积神经网络（CNN）已被广泛用作图像相关任务的最新模型，但它们的预测通常对小的输入扰动高度敏感，人类视觉对这些扰动具有鲁棒性。本文介绍了Perferter，这是一个基于web的应用程序，允许用户即时探索当3D输入场景受到交互干扰时CNN的激活和预测是如何演变的。扰动器提供了大量场景修改，如摄影机控制、照明和着色效果、背景修改、对象变形以及对抗性攻击，以便于发现潜在漏洞。微调后的模型版本可以直接进行比较，以便对其稳健性进行定性评估。机器学习专家的案例研究表明，扰动器可以帮助用户快速生成关于模型漏洞的假设，并定性地比较模型行为。通过定量分析，我们可以用其他CNN体系结构和输入图像复制用户的见解，从而得出关于经过对抗训练的模型的脆弱性的新见解。 摘要：While convolutional neural networks (CNNs) have found wide adoption as state-of-the-art models for image-related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against. This paper presents Perturber, a web-based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed. Perturber offers a large variety of scene modifications, such as camera controls, lighting and shading effects, background modifications, object morphing, as well as adversarial attacks, to facilitate the discovery of potential vulnerabilities. Fine-tuned model versions can be directly compared for qualitative evaluation of their robustness. Case studies with machine learning experts have shown that Perturber helps users to quickly generate hypotheses about model vulnerabilities and to qualitatively compare model behavior. Using quantitative analyses, we could replicate users' insights with other CNN architectures and input images, yielding new insights about the vulnerability of adversarially trained models.

【52】 Towards Understanding the Data Dependency of Mixup-style Training 标题：迈向对混合式训练的数据依赖性的理解 链接：https://arxiv.org/abs/2110.07647

作者：Muthu Chidambaram,Xiang Wang,Yuzheng Hu,Chenwei Wu,Rong Ge 机构：Department of Computer Science, Duke University, School of Mathematical Sciences, Peking University 备注：25 pages, 13 figures 摘要：在混合训练范式中，使用数据点及其相关标签的凸组合训练模型。尽管在训练过程中几乎看不到真实的数据点，但与标准训练相比，使用混合训练的模型似乎仍然能够最小化原始经验风险，并在各种任务上表现出更好的泛化性和鲁棒性。在本文中，我们研究了混合训练的这些好处如何依赖于分类环境中数据的属性。为了最小化原始经验风险，我们计算了混合最优分类的闭合形式，这允许我们构造一个简单的数据集，在该数据集上最小化混合损失可以证明学习一个不最小化数据上的经验损失的分类器。另一方面，我们也给出了混合训练的充分条件，使原始经验风险最小化。对于泛化，我们描述了混合分类器的边界，并使用它来理解为什么与标准训练相比，混合分类器的决策边界能够更好地适应训练数据的完整结构。相比之下，我们还表明，对于一大类线性模型和线性可分离数据集，混合训练导致学习与标准训练相同的分类器。 摘要：In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training. In this paper, we investigate how these benefits of Mixup training rely on properties of the data in the context of classification. For minimizing the original empirical risk, we compute a closed form for the Mixup-optimal classification, which allows us to construct a simple dataset on which minimizing the Mixup loss can provably lead to learning a classifier that does not minimize the empirical loss on the data. On the other hand, we also give sufficient conditions for Mixup training to also minimize the original empirical risk. For generalization, we characterize the margin of a Mixup classifier, and use this to understand why the decision boundary of a Mixup classifier can adapt better to the full structure of the training data when compared to standard training. In contrast, we also show that, for a large class of linear models and linearly separable datasets, Mixup training leads to learning the same classifier as standard training.

【53】 Non-deep Networks 标题：非深度网络 链接：https://arxiv.org/abs/2110.07641

作者：Ankit Goyal,Alexey Bochkovskiy,Jia Deng,Vladlen Koltun 机构：Princeton University, Intel Labs 摘要：深度是深度神经网络的特征。但深度越深意味着顺序计算越多，延迟越大。这就引出了一个问题——有可能建立高性能的“非深度”神经网络吗？我们证明了这一点。为此，我们使用并行子网络，而不是一层接一层地堆叠。这有助于在保持高性能的同时有效减少深度。通过利用并行子结构，我们首次表明，深度仅为12的网络可以在ImageNet上达到80%以上的顶级精度，在CIFAR10上达到96%，在CIFAR100上达到81%。我们还表明，具有低深度（12）主干的网络可以在MS-COCO上实现48%的AP。我们分析了设计中的缩放规则，并展示了如何在不改变网络深度的情况下提高性能。最后，我们提供了如何使用非深度网络构建低延迟识别系统的概念证明。代码可在https://github.com/imankgoyal/NonDeepNetworks. 摘要：Depth is the hallmark of deep neural networks. But more depth means more sequential computation and higher latency. This begs the question -- is it possible to build high-performing "non-deep" neural networks? We show that it is. To do so, we use parallel subnetworks instead of stacking one layer after another. This helps effectively reduce depth while maintaining high performance. By utilizing parallel substructures, we show, for the first time, that a network with a depth of just 12 can achieve top-1 accuracy over 80% on ImageNet, 96% on CIFAR10, and 81% on CIFAR100. We also show that a network with a low-depth (12) backbone can achieve an AP of 48% on MS-COCO. We analyze the scaling rules for our design and show how to increase performance without changing the network's depth. Finally, we provide a proof of concept for how non-deep networks could be used to build low-latency recognition systems. Code is available at https://github.com/imankgoyal/NonDeepNetworks.

【54】 Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation Algorithm 标题：用量子量子比特旋转算法高效求解最大割问题 链接：https://arxiv.org/abs/2110.08016

作者：Xin Wang 机构：Key Laboratory of Systems and Control, Academy of Mathematics and, Systems Science, Chinese Academy of Sciences, Beijing , P. R. China, University of Chinese Academy of Sciences, Beijing , P. R. China 备注：6 pages, 3 figures 摘要：优化参数化量子电路有望有效利用近期量子计算机实现潜在的量子优势。然而，在参数ansatz的可表达性和可训练性之间存在着臭名昭著的折衷。我们发现，在组合优化问题中，由于解是用位字符串描述的，因此可以用ansatz的表达能力换取高可训练性。具体地说，通过关注最大割问题，我们介绍了一种简单而有效的算法，称为量子比特旋转算法（QQRA）。量子电路由单个量子比特旋转门组成，在每个量子比特上实现。闸门的旋转角度可以在没有贫瘠高原的情况下进行训练。因此，可以在概率接近1的情况下获得最大割问题的近似解。为了说明QQRA的有效性，我们将其与著名的量子近似优化算法和经典的Goemans-Williamson算法进行了比较。 摘要：Optimizing parameterized quantum circuits promises efficient use of near-term quantum computers to achieve the potential quantum advantage. However, there is a notorious tradeoff between the expressibility and trainability of the parameter ansatz. We find that in combinatorial optimization problems, since the solutions are described by bit strings, one can trade the expressiveness of the ansatz for high trainability. To be specific, by focusing on the max-cut problem we introduce a simple yet efficient algorithm named Quantum Qubit Rotation Algorithm (QQRA). The quantum circuits are comprised with single-qubit rotation gates implementing on each qubit. The rotation angles of the gates can be trained free of barren plateaus. Thus, the approximate solution of the max-cut problem can be obtained with probability close to 1. To illustrate the effectiveness of QQRA, we compare it with the well known quantum approximate optimization algorithm and the classical Goemans-Williamson algorithm.

机器翻译，仅供参考